Brain function analysis apparatus

ABSTRACT

There is provided a method including, acquiring brain function data and diffusion tensor data (S 10 ), calculating a connection degree between voxels adjacent to each other based on the diffusion tensor data (S 30 ), constituting a data evaluation value based on the brain function data and the connection degree between the adjacent voxels (S 40 ), subjecting the data evaluation value to nonparametric regression analysis (S 50 ), and forming and displaying images based on results of the analysis (S 60 , S 70 ).

TECHNICAL FIELD

The present invention relates to a brain function analysis method and abrain function analysis program for analyzing brain functions utilizingvarious kinds of data obtained by measurement techniques such as fMRI(functional Magnetic Resonance Imaging) or PET (Positron EmissionTomography).

BACKGROUND ART

While typical non-invasive methods for measuring brain functions includefMRI, PET, MEG (magnetoencephalography) at present, the fMRI isconsidered to have the highest spatial resolution of data among them andis widely used.

The fMRI is a method for imaging various kinds of physical quantityallowing to locate activated brain regions as a measuring quantity, andis an effective technique for measuring brain functions (refer toNon-patent Documents 1 and 2). While reflecting the proton density,longitudinal relaxation time T₁, and transverse relaxation time T₂ oftissues in a living body as same as the principle of an anatomical MRIwhich images a brain structure, fMRI particularly has a feature todetect an increase in blood flow volume in an activated brain region. Itis known that the blood flow volume will locally increase in theactivated brain region and hemoglobin in blood differs in its magneticproperties between a state that oxygen is bound thereto (oxygenatedhemoglobin) and a state that it is released therefrom (deoxygenatedhemoglobin). It is considered that fMRI signals (BOLD signals) in anactive region will increase since the amount of deoxygenated hemoglobinwhich disturbs a magnetic field is decreased in the increased arterialblood. Hence, the use of fMRI can locate regions in the brain related tothe task (active region), by following a change in BOLD signal when asubject is performing a certain task.

Typical techniques to analyze the time series data of BOLD signalsmeasured by fMRI includes SPM (Statistical Parametric Mapping) based ona general linear model (refer to Non-patent Document 1), a data analysisbased on a principal component analysis and an independent componentanalysis (refer to Non-patent Document 3). These techniques havefeatures in which results in which the time series data of the BOLDsignals is individually subjected to statistical processing for everythree-dimensional pixel (Voxel) is output as an image to locateactivated brain regions.

In the above analysis techniques, however, there exists an issue thatthe network structures of neurons are not taken into consideration uponanalyzing data. Many neurons constitute a complicated network viasynapses in a brain, and it is considered that the brain realizes ahigher brain function as a whole by mutual cooperation of each regionthereof via such a neural network according to recent knowledge of brainscience. For example, when the subject performs a certain task, it isobserved a phenomenon that a plurality of regions are activated in thebrain. Then, the analysis of this phenomenon using the above analysistechniques enables to locate each active region, but it is difficult tolocate a connection between the active regions.

This has an underlying cause as follows. Since BOLD signals measured byfMRI are based on blood flow volume as described above, the activity ofa gray matter (cell body of the neuron) having a relatively high bloodflow volume in the brain is captured, but the activity of a white matter(axon of the neuron, or nerve fiber) having relatively low blood flowvolume is hardly captured by fMRI.

Meanwhile, as a method for capturing a running direction of a nervefiber group, which is the basis of the neural network structures, it hasrecently drawn attention a DTI (Diffusion Tensor Imaging) method inwhich the degree of proton diffusion in a body tissue is measured as anew observation quantity of MRI (refer to Non-patent Document 4). When anormal anatomical MRI is utilized, a nerve fiber becomes a high signalin a T₁ weighted image and a low signal in T₂ weighted image. The reasonthat the nerve fiber is converted into the high signal in the T₁weighted image is in presence of myelin. The myelin is composed of alipid with a double-layer structure and a huge protein and takes a formto arrange along the running direction of the nerve fiber. Hence, thereoccurs anisotropy that the diffusion constant of protons is large in therunning direction of the nerve fiber and is small in a directionperpendicular to it. DTI is the technique of measuring the anisotropy ofdiffusion by applying MPG (Motion Probing Gradient): {right arrow over(G)}=(G_(x),G_(y),G_(z))^(T) in order to emphasize the diffusion ofprotons. It is the technique, for example, to measure the intensity S′(l, m, k, i) of a BOLD signal obtained by ST (Stejskal-Tanner) pulsesequence in which STG (Stejskal-Tanner Gradient) pulses for diffusiondetection are added to before and after SE (Spin Echo) pulses. Here “l”,“m” and “k” are positive discrete variables representing the position ofa voxel and represent an X direction, Y direction and Z direction of thevoxel, respectively. In addition, “i” is a positive discrete variablerepresenting a measuring time. The intensity S′(l, m, k, i) of the BOLDsignal can be written as

S′(l,m,k,i)=ρ′(l,m,k,i)exp(−b{right arrow over (G)} ^(T) D(l,m,k){rightarrow over (G)}).  (1)

When a diffusion weighted image is generated, the diffusion tensor:

$\begin{matrix}{{D\left( {l,m,k} \right)} = \begin{pmatrix}{D_{ll}\left( {l,m,k} \right)} & {D_{lm}\left( {l,m,k} \right)} & {D_{lk}\left( {l,m,k} \right)} \\{D_{ml}\left( {l,m,k} \right)} & {D_{mm}\left( {l,m,k} \right)} & {D_{mk}\left( {l,m,k} \right)} \\{D_{kl}\left( {l,m,k} \right)} & {D_{km}\left( {l,m,k} \right)} & {D_{kk}\left( {l,m,k} \right)}\end{pmatrix}} & (2)\end{matrix}$

in Equation (1) will be an object of the data analysis. Here ρ′(l, m, k,i) represents the intensity of a BOLD signal with no application of MPG,which is an object of data analysis in the normal brain functionanalysis, and “b” is a parameter representing the strength of MPG. Notethat ρ′(l, m, k, i) is represented by

$\begin{matrix}{{\rho^{\prime}\left( {l,m,k,i} \right)} \propto {{f(v)} \cdot {\xi^{\prime}\left( {l,m,k,i} \right)} \cdot \left( {1 - {\exp \left( {- \frac{T_{R}}{T_{1}}} \right)}} \right) \cdot {\exp \left( {- \; \frac{T_{E}}{T_{2}}} \right)}}} & (3)\end{matrix}$

where f(v) represents a flow velocity, TR a repetition time, TE an echotime; and ξ′(l, m, k, i) a proton density.

There has been currently used a brain function analysis method in whichRegions of Interest (ROI) in a brain are connected by a diffusion tensordata D(l, m, k) after analyzing the time series data of BOLD signalsρ′(l, m, k, i) obtained by fMRI measurement (refer to Non-patentDocument 5).

Non-patent Document 1: “Human Brain Function: 2nd-Ed.”, Richard S. J.Frackowiak, et al, ELSEVIER ACADEMIC PRESS, 2004

Non-Patent Document 2: “Image of Mind”, M. I. Posner and M. E. Raichle,W H Freeman & Co, 1997

Non-patent Document 3: “Independent Component Analysis: Theory andApplications”, T. W. Lee, Kluwer Acadmic, 1988

Non-patent Document 4: “Korede wakaru diffusion MRI” S. Aoki, O. Abe,Syuujyun sha, 2002

Non-patent Document 5: “Combined functional MRI and tractography todemonstrate the connectivity of the human primary motor cortex in vivo”,Guye M, et al., Neuroimage, Vol. 19, pp. 1349-1360, 2003

DISCLOSURE OF THE INVENTION

In the above brain function analysis method, however, the diffusiontensor data D(l, m, k) is not used for the analysis of the time seriesdata of the BOLD signals ρ′(l, m, k, i) itself, thus there are strongdoubts about its effectiveness.

The present invention is made in view of the above problems, and it aimsat providing a brain function analysis method and a brain functionanalysis program capable of analyzing brain function data also inconsideration of a connection structure between activated brain regionsbased on the brain function data measured by a non-invasive measuringmethod, such as fMRI, PET, or the like, and diffusion tensor datacapable of specifying a running direction of a nerve fiber group.

One aspect of the present invention is to provide a brain functionanalysis method, including: a first step of acquiring, on avoxel-by-voxel basis, brain function data capable of locating activatedbrain regions, and diffusion tensor data capable of specifying a degreeof diffusion of protons within the brain; a second step of constitutingan evaluation value of a connection degree between voxels adjacent toeach other based on the diffusion tensor data; and a third step ofanalyzing the brain function data based on the evaluation value of theconnection degree between the adjacent voxels.

Another aspect of the present invention is to provide a brain functionanalysis program causing a computer to function as: brain function dataacquisition means for acquiring, on a voxel-by-voxel basis, brainfunction data capable of locating activated brain regions; diffusiontensor data acquisition means for acquiring, on a voxel-by-voxel basis,diffusion tensor data capable of specifying a degree of diffusion ofprotons within the brain; data evaluation value constitution means forconstituting an evaluation value of the connection degree between voxelsadjacent to each other based on the diffusion tensor data; and dataanalysis means for analyzing the brain function data based on theevaluation value of the connection degree between the adjacent voxels.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a first embodiment of thepresent invention;

FIG. 2 is a view illustrating a method of typical fMRI measurement;

FIG. 3 is a view illustrating a typical fMRI image (two-dimensionalslice image), wherein FIG. 3A shows a head of a subject on a bed, andFIG. 3B shows a voxel composing a two-dimensional cross section(two-dimensional slice image) of the head;

FIG. 4 is a table illustrating time series data and diffusion tensordata of brain function information on the voxels composing thetwo-dimensional slice image;

FIG. 5 is a table showing one example of the time series data of thebrain function information shown in FIG. 4, wherein FIG. 5A shows actualmeasurement values, and FIG. 5B shows values obtained by binarizing theactual measurement values based on a threshold;

FIG. 6 is a view of a stereoscopic configuration of the time series dataand the diffusion tensor data of the brain function information shown inFIG. 4, wherein FIG. 6A shows the head of the subject on the bed, andFIG. 6B shows a procedure for generating a k-th two-dimensional sliceimage S_(k) from the time series data and the diffusion tensor data ofthe brain function information shown in FIG. 4;

FIG. 7 is a view illustrating a head image which is three-dimensionallyconfigured from the two-dimensional slice images S_(k) shown in FIG. 6,wherein FIG. 7A shows the head of the subject on the bed, and FIG. 7Bshows a head image which is three-dimensionally configured by collectingthe two-dimensional slice images S_(k);

FIG. 8 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 1;

FIG. 9 is a view showing one example of a data preprocessing techniquein accordance with the present invention, wherein FIG. 9A shows datapreprocessing in an area where both of two voxels adjacent to a certainvoxel are regarded as gray matters, FIG. 9B shows data preprocessing inan area where only one of the two voxels is regarded as the gray matter,and FIG. 9C shows data preprocessing in an area where neither of twovoxels is regarded as the gray matter;

FIG. 10 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a second embodiment ofthe present invention;

FIG. 11 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 10;

FIG. 12 is a view illustrating a smoothing technique performed by datasmoothing means shown in FIG. 10;

FIG. 13 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a modified example of thesecond embodiment;

FIG. 14 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 13;

FIG. 15 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a third embodiment of thepresent invention;

FIG. 16 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 15;

FIG. 17 is a view illustrating a clustering technique performed byclustering means 300 shown in FIG. 15, wherein FIG. 17A shows values ofconnection degree vectors before performing clustering processing, andFIG. 17B shows values of connection vectors after performing theclustering processing;

FIG. 18 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a modified example of thethird embodiment;

FIG. 19 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 18;

FIG. 20 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a fourth embodiment ofthe present invention;

FIG. 21 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 20;

FIG. 22 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a modified example of thefourth embodiment;

FIG. 23 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 22; and

FIG. 24 is a view showing results of various kinds of brain functionanalyses performed when the subject is made to perform simple repetitionusing acoustic stimulations, wherein FIGS. 24A and 24B both show resultsof a T-test using SPM (FIG. 24A shows a case where a threshold of theT-test is corrected, whereas FIG. 24B is a case where the threshold ofthe T-test is not corrected), FIG. 24C shows analysis results using abrain function analysis technique in which SPM and tractography arecombined, and FIG. 24D shows analysis results using a brain functionanalysis method in accordance with the first embodiment of the presentinvention.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention is characterized by acquiring brain function dataand diffusion tensor data on a voxel-by-voxel basis from a non-invasivemeasuring apparatus such as MRI or the like in order to locate activatedbrain regions, constituting an evaluation value of a connection degreebetween voxels adjacent to each other from the diffusion tensor data,and analyzing the brain function data using the evaluation value of theconnection degree between the adjacent voxels. The evaluation value ofthe connection degree between the adjacent voxels of the above brainfunction data is variously considered depending on data analysistechniques, and how to use the evaluation value of the connection degreebetween voxels adjacent to each other is also variously considereddepending on data analysis techniques.

There will be described embodiments of the present invention in detailwith reference to the drawings.

First Embodiment

FIG. 1 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a first embodiment of thepresent invention. A brain function analysis apparatus 10 of the firstembodiment is provided with brain function data acquisition means 1 foracquiring original time series data ρ′(l, m, k, i) (for example,Equation (3)) of brain function information which is measured by an MRIdevice 50; diffusion tensor data acquisition means 2 for acquiringdiffusion tensor data D(l, m, k) (for example, Equation (2)) which ismeasured also by the MRI device 50; data preprocessing means 3 forpre-processing the original time series data ρ′(l, m, k, i) of the brainfunction information which is acquired by the brain function dataacquisition means 1; inter-voxel connection degree calculation means 4for calculating a connection degree vector {right arrow over (C)}(l,m,k)representing a connection degree between voxels adjacent to each otherfrom the diffusion tensor data D(l, m, k) which is acquired by thediffusion tensor data acquisition means 2; data evaluation valueconstitution means 5 for constituting a predetermined evaluation value Qfrom the time series data ρ(l, m, k, i) of the brain functioninformation which is pre-processed by the data preprocessing means 3,and the connection degree vector {right arrow over (C)}(l,m,k), which iscalculated by the inter-voxel connection degree calculation means 4;data analysis means 6 for performing a calculation to determine theextremum (minimum value in the present embodiment) of the dataevaluation value Q; image generation means 7 for generating images basedon the data which is calculated by the data analysis means 6; imagedisplay means 8 for displaying the images which are generated by theimage generation means 7; memory means 9 composed of a sub memory forrecording various data which is acquired, calculated, constituted,analyzed, and generated by the above respective means 2 to 8, and a mainmemory for storing a computer readable program for brain functionanalysis for executing each of steps explained in full detailhenceforth; a CPU (Central Processing Unit) for controlling each of theabove means 2 to 9 according to the program for brain function analysiswhich is read from the memory means 9; and the like. Incidentally,meaning of each of above-described symbols will be described below.

Here the MRI device 50 is composed of a magnet assembly which iscomposed of a static magnetic field coil for generating a staticmagnetic field for causing Nuclear Magnetic Resonance (NMR), a highfrequency coil for irradiating high frequency waves of resonancefrequency to detect a resonance signal, a gradient coil for generating agradient magnetic field for encoding position information to a resonancesignal, and the like; and a system controller for controllingenergization of these coils; and the like, wherein the MRI device 50generates various kinds of fMRI data (original time series data ρ′(l, m,k, i) of the brain function information relevant to a blood flow volume,diffusion tensor data D(l, m, k) relevant to a running direction ofnerve fibers, or the like) according to requests of an operator thereof,and transmits these kinds of data to the brain function analysisapparatus 10. Note that in the present invention it is not alwaysnecessary to acquire the original time series data ρ′(l, m, k, i) of thebrain function data from the MRI device 50, but there may be employed aconfiguration of acquiring it from a non-invasive measuring apparatus,such as PET which can also generate various kinds of brain functiondata, instead of that.

Meanwhile, as for the image display means 8, various display units suchas CRT displays, TFT displays, plasma displays, or the like, and variousprinters such as ink jet printers, laser printers, or the like areavailable, for example. In addition, the memory means 9 is composed of,for example, RAM (Random Access Memory), ROM (Read Only Memory), or thelike. Further, the sub memory and the main memory of recording means 9are separately composed, and thus the contents stored in the main memorymay be stored in optical disks such as magnetic hard disks, floppy(registered trademark) disks, or CD-ROMs, magnetic tapes, memory chips,or the like.

Although the brain function analysis apparatus 10 employs a displayconsole type in which major constitution means 10A of the presentembodiment, the image display means 8, and the memory means 9 areintegrated in the present embodiment, there may be employed such aconfiguration that the image display means 8, or the image display means8 and the memory means 9 are separated from the major constitution means10A as respectively independent image display unit and storage unit. Thebrain function analysis apparatus 10 is achieved by the computer in anyconstitutions, and each of the above means 2 to 9 is controlledaccording to the program for brain function analysis which is read fromthe memory means 9 by a CPU (Central Processing Unit) 100.

Here the computer refers to a unit which processes structured inputs inaccordance with a predetermined rule to then structure and outputprocessed results, and for example, a general-purpose computer, asupercomputer, a mainframe, a workstation, a microcomputer, a server, orthe like is included therein. Additionally, it may be a configuration(for example, a distributed computer system) in which two or morecomputers connected through communication networks (for example,intranet, local area network (LAN)), wide area network (WAN), andcommunication networks composed of combinations thereof are composed ofeach other.

Next, in order to facilitate understanding of the present invention, anoutline of a flow of the brain function analysis according to thepresent invention will be described using FIG. 2 through FIG. 7. FIG. 2illustrates a method of typical fMRI measurement, FIG. 3 illustrates atypical fMRI image (two-dimensional slice image), FIG. 3A shows a headof a subject on a bed, and FIG. 3B shows voxels composing atwo-dimensional cross section (two-dimensional slice image) of the head.

For example, it is considered a case where when letting the subject toperform a certain task (for example, tapping of fingers), regions in thebrain to relevant to the task is located by the fMRI measurement. In theexample shown in FIG. 2, there is shown a case where one set is repeatedthree times in one measurement, where one set is composed of a task of acertain period of time “T” and a rest of a certain period of time “T”the same as the task thereof. Here a horizontal axis is a time-axis,wherein “ON” indicates the task and “OFF” indicates the rest. fMRImeasurement is usually performed several tens of times in oneexperiment. Although the fMRI measurement is performed 24 times in thisexample, images of the task start and the task end are not normally usedin consideration of a lag of measurement time, so that it means thatsignificant fMRI measurement has been performed 18 times (correspondingto 18 thick lines) actually. Incidentally, “t_(i) (for i=1, 2, . . . ,24)” discretely shows actual times of the measurement. “t_(i)” will behereinafter represented only as “i (for i=1, 2, . . . , I)” (positivediscrete integer).

A two-dimensional slice image S_(k) as shown in FIG. 3B is obtained as aresult of such fMRI measurement. Here “k” indicates the number oftwo-dimensional slice images obtained by one experiment, and 20 to 30two-dimensional slice images are obtained in the normal fMRI measurement(in the drawing, K=20 to 30). Although these two-dimensional sliceimages S_(k) (for k=1, 2, . . . , K) are superficially described in FIG.3B, they are actually composed of three-dimensional stereoscopic pixelscalled voxel. Although the two-dimensional slice image composed of 64×64voxels is shown in the figure as one example, it may be, of course, atwo-dimensional slice image divided into the different number of voxels.Although the voxel on an arbitrary two-dimensional slice image S_(k) cangenerally be represented as (l, m) using two positive discrete variablesof “l” and “m”, this can also be represented with single positivediscrete variable “j” by assigning a number thereto under apredetermined rule. Namely, it is mathematically possible to provideone-to-one correspondence between “j” and (l, m) (j≡(l, m) (for S_(k)))under the predetermined rule. In this example, since an upper-limit “L”of “l” and an upper-limit “M” of “m” are both 64 (L=64, M=64), itresults in j=1, 2, . . . , 4096. Hereinafter, these notations will beboth used suitably according to the case. In addition, a circle shown inFIG. 3B represents an outline of a cross-sectional shape of the head.

FIG. 4 illustrates the time series data ρ(j, k, i) (≡ρ_(k)(j, i)) of thebrain function information on the voxel “j” composing a certaintwo-dimensional slice image S_(k), which is acquired by the brainfunction data acquisition means 1 and pre-processed by the datapreprocessing section 3 (will be described later), and D(j, k)(≡D_(k)(j)) which is acquired by the diffusion tensor data acquisitionmeans 2. As described in the background art, when the diffusion weightedimage is generated by the DTI measurement, the original time series dataρ′(l, m, k, i) (≡ρ′_(k)(l, m, i)) of the brain function information onthe two-dimensional slice image S_(k) and the diffusion tensor data D(l,m, k) (≡D_(k) (l, m)) are typically obtained as a result of imaging byan ST pulse sequence obtained by adding STG pulses for diffusiondetection to before and after an SE pulse. ρ_(k)(j, i) represents thetime series data of the brain function information which is obtained asa result of performing predetermined preprocessing, which will bedescribed later, to this original time series data ρ′_(k)(l, m, i).Namely, ρ_(k)(j, i) in the drawing represents the brain functioninformation at time “i” in the voxel “j” of a certain two-dimensionalslice image S_(k). Meanwhile, D_(k)(j) represents the diffusion tensorinformation in the voxel “j” of the certain two-dimensional slice imageS_(k). ρ_(k)(j, i) is specifically represented as a scalar, whileD_(k)(j) is represented in a matrix as shown in Equation (2), and theywill take, for example, the following values:

$\begin{matrix}{{{\rho_{k}\left( {j,i} \right)} = 5000},{{D_{k}(j)} = {\begin{pmatrix}1.0 & 1.2 & 1.1 \\1.2 & 0.8 & 0.9 \\1.1 & 0.9 & 0.7\end{pmatrix}.}}} & (4)\end{matrix}$

Since the diffusion tensor is typically a symmetric tensor (non-diagonalelements are symmetrical) as shown in this example, it willsubstantially have six components. Additionally, CLASS in the drawingrepresents whether or not the task is performed, where “ON (=1)” and“OFF (=0)” correspond to “ON” and “OFF” shown in FIG. 2, respectively.

FIG. 5 is a table showing one example of the time series data ρ_(k)(j,i) of the brain function information shown in FIG. 4. Although actualtime series data of the brain function information ρ_(k)(j, i) takescontinuous values as illustrated in FIG. 5(A), once a value “4000” isset as an activity threshold, and then the data may be further binarizedat the stage of the preprocessing such that a voxel value that takes avalue larger than that is an activity “A”, and a voxel value that takesa value smaller than that is an inactive “I”, as shown in for exampleFIG. 5(B).

FIG. 6 is a view of a stereoscopic configuration of the diffusion tensordata D_(k)(j) shown in FIG. 4 and the time series data ρ_(k)(j, i) ofthe brain function information also shown in FIG. 4, wherein FIG. 6Ashows the head of the subject on the bed, and FIG. 6B shows a procedurefor generating a k-th two-dimensional slice image S_(k) from the timeseries data and the diffusion tensor data of the brain functioninformation shown in FIG. 4. Pentagons on voxels “j1” and “j2” in FIG.6B illustrate internal spaces (namely, a scalar field and a tensor fieldon the voxel) on each voxel, to which the time series data ρ_(k)(j, i)and the diffusion tensor data D_(k)(j) of the brain function informationshown in FIG. 4 attach.

For example, in SPM described in the background art, after performingpreprocessing such as

(a) Realignment: subsequent images are aligned with a toptwo-dimensional slice image (for example, S1), so that positioncompensation associated with movements of the head under measurement iscarried out, and a false signal due to the movements is removed,

(b) Spatial Normalization: in order to gather or compare data of aplurality of subjects, the data of each subject is adjusted to aTalairach standard brain, and

(c) Smoothing: a Gaussian type filter is applied to a noisy originaltime series data, so that sensitivity of the analysis is improvedwithout reducing spatial resolution, to the original time series dataρ′_(k)(l, m, i) of the brain function information acquired from the MRIdevice 50, various kinds of test or the like are performed for everyvoxel, and significant active regions associated with the task arefurther tested between groups composed of a plurality of subjects, bypersonal data of the subject.

Further, in SPM an activity estimation model is prepared in advance, andhow much the time series data ρ_(k)(j, i) (=ρ_(k)(l, m, i)) of thefunction data pre-processed as described above matches with the model issubjected to a parameter estimation using a general linear model withoutusing the diffusion tensor data D_(k)(l, m).

Meanwhile, in the present invention, preprocessing (d) which will beexplained in full detail hereinafter is performed on the original timeseries data p′_(k)(l, m, i) of the brain function information using thediffusion tensor data D_(k)(j) (=D_(k)(l, m)) acquired by the diffusiontensor data acquisition means 2, other than the above preprocessing.Subsequently, data processing which will be explained in full detailhereinafter is performed on the pre-processed time series data ρ_(k)(j,i) (=ρ_(k)(l, m, i)) of the brain function information using thediffusion tensor data D_(k)(j) (=D_(k)(l, m)), and thus two-dimensionalimage data a_(k)(j) (=a_(k)(l, m)) which is a target of an image displayis generated.

Note that although the two-dimensional slice image is described as anexample for the purpose of simplification in the above, a stereoscopicimage data a_(k)(j) (=a(j, k)=a(n)) of the head as shown in FIGS. 7A and7B is three-dimensionally subjected to data analysis in practice. Thedata processing using the diffusion tensor data D_(k)(j) as will beexplained in full detail hereinafter is performed also in that case.Here “n” is a positive discrete variable that represents the position ofthe voxel, in which numbers are assigned to all the voxels which arestereoscopically configured, under the predetermined rule. Although thevariable “j” which represents the position of the two-dimensional sliceimage on an XY coordinate and the variable “k” which represents adirection of a third dimension (z direction) are separately described inthe drawing, it is mathematically equivalent to represent this withabove 1 variable “n” (n≡(j, k)). Note that a notation using 1 variable“n” and a notation using three variables (j, l, k) in an XYZ coordinatesystem for representing the same three-dimensional voxel will be bothused suitably hereinafter (n≡(l, m, k)≡(j, k)).

A data analysis method performed by the brain function analysisapparatus 10 shown in FIG. 1 will be described in detail on thepresupposition described above. FIG. 8 is a flow chart showing a dataanalysis procedure performed by the brain function analysis apparatus 10shown in FIG. 1.

At Step S10, the brain function data acquisition means 1 acquires theoriginal time series data ρ′(l, m, k, i) of the brain functioninformation measured by the MRI device 50. At the same step, thediffusion tensor information acquisition means 2 acquires the diffusiontensor data D(l, m, k) measured also by the MRI device 50.

Next, the data preprocessing means 3 performs preprocessing such asposition compensation, noise rejection, or the like represented by theabove techniques (a) to (c) of SPM, on the original time series dataρ′(l, m, k, i) of the brain function information acquired as describedabove at Step S20.

At the same step, in order to further perform the preprocessing (d)(data conversion to nerve signal) on the time series data ρ′(l, m, k, i)of the brain function information to which the normal preprocessing isperformed as described above, the data preprocessing means 3 calculates,for example, an eigenvector {right arrow over (v)}_(M)(l,m,k)corresponding to the maximum eigenvalue λ_(M) among eigenvectors {rightarrow over (v)}_(α)(l,m,k) (α=1, 2, 3) which satisfy the following eigenequation of the diffusion tensor data D(l, m, k):

(l,m,k){right arrow over (v)}_(α)(l,m,k)=λ_(α){right arrow over(v)}_(α)(l,m,k),  (5)

using the diffusion tensor data D(l, m, k) acquired as described above.

For the purpose of simplification, it is assumed hereinafter that|λ₁|≧|λ₂|≧|λ₃|, and an eigenvector {right arrow over (v)}₁(l,m,k)corresponding to λ₁ which is the maximum eigenvalue will be written as{right arrow over (v)}_(M)(l,m,k). In addition, a norm of theeigenvector {right arrow over (v)}_(M)(l,m,k) shall be normalized by 1.

Subsequently, at the same step, the data preprocessing means 3, withrespect to a certain voxel (l, m, k), first determines an adjacent voxelcorresponding a direction vector in which an absolute value of an innerproduct with the eigenvector {right arrow over (v)}_(M)(l,m,k) on thevoxel (l, m, k) becomes the maximum among 26 voxels (three dimension)which are adjacent to the voxel (l, m, k).

It is assumed that the adjacent voxel that satisfies this condition is(l+1, m+1, k), for example. An adjacent voxel having a point-symmetricrelation with respect to the voxel thus determined about the voxel (l,m, k) is then determined. In this case, it is a voxel (l−1, m−1, k).This situation is shown in FIG. 9. Since the voxel (l+1, m+1, k) and thevoxel (l−1, m−1, k) are voxels whose inner product with the eigenvector{right arrow over (v)}_(M)(l,m,k) on the voxel (l, m, k) becomes themaximum as described above, it is considered that the connection withthe voxel (l, m, k) is the strongest.

The reason is that if it is a case shown in FIG. 9( c), the voxel (l+1,m+1, k) and the voxel (l−1, m−1, k) about the voxel (l, m, k) can beregarded as the voxels on the nerve fibers running in the same runningdirection.

Next, at the same step, the data preprocessing means 3 rewrites the timeseries data ρ′(l, m, k, i) of the brain function information on thevoxel (l, m, k) according to following three kinds of situations.

(Situation 1) a case where the voxel (l+1, m+1, k) and the voxel (l−1,m−1, k) are both regarded as a gray matter (cell body of the neuron: Gin the drawing) from the predetermined anatomical data of the brain (T1weighted image acquired by the MRI device 50) acquired in advance (referto FIG. 9(A)):

In this case, the value of the time series data ρ′(l, m, k, i) of thebrain function information on the voxel (l, m, k) is rewritten by

$\begin{matrix}{{\rho \left( {l,m,k,i} \right)} = {\frac{{\rho^{\prime}\left( {{l + 1},m,{+ 1},k,i} \right)} + {\rho^{\prime}\left( {{l - 1},{m - 1},k,i} \right)}}{2}.}} & (6)\end{matrix}$

(Situation 2) a case where the voxel (l+1, m+1, k) (or the voxel (l−1,m−1, k)) is regarded as a gray matter from the predetermined anatomicaldata of the brain (T1 weighted image acquired by the MRI device 50)acquired in advance (refer to FIG. 9(B)):

In this case, the value of time series data ρ′(l, m, k, i) of the brainfunction information on the voxel (l, m, k) is rewritten by

ρ(l,m,k,i)=ρ′(l+1,m+1,k,i)(or ρ′(l−1,m−1,k,i)).  (7)

(Situation 3) a case where the voxel (l+1, m+1, k) and the voxel (l−1,m−1, k) are neither regarded as a gray matter (namely, regarded as awhite matter (nerve fiber of the nerve: W in the drawing)) from thepredetermined anatomical data of the brain (T1 weighted image acquiredby the MRI device 50) acquired in advance (refer to FIG. 9(C)):

In this case, the value of time series data ρ′(l, m, k, i) of the brainfunction information on the voxel (l, m, k) is rewritten by

$\begin{matrix}{{\rho \left( {l,m,k,i} \right)} = {\frac{\mspace{20mu} \begin{matrix}{{\rho^{\prime}\left( {{l + 1},m,{+ 1},k,i} \right)} +} \\{{\rho^{\prime}\left( {l,m,k,i} \right)} + {\rho^{\prime}\left( {{l - 1},{m - 1},k,i} \right)}}\end{matrix}}{3}.}} & (8)\end{matrix}$

Such processing is performed on all the voxels. As a result of this, thevalue of the time series data of the brain function information on thevoxel (gray matter voxel) regarded as the gray matter can be reflected(transmitted) to the value of the time series data of the brain functioninformation on the adjacent voxel (white matter voxel) regarded as thewhite matter as shown in lower figures of FIGS. 9(A) and (B).Furthermore, when the voxels regarded as the white matter are adjacentto each other as shown in lower figure of FIG. 9(C), values of the brainfunction data on these voxels will be smoothed. Effects of subsequentsteps can be accentuated by performing such preprocessing.

Note that, how to performing such preprocessing (d) is not limited tothe example described above, but the white matter voxel which isadjacent to the gray matter voxel and receives the value of the timeseries data of the brain function information of the gray matter voxelmay further transmit the value sequentially to the adjacent white mattervoxel in a procedure similar to that described above, for example.Moreover, it may be configured such that a weighted value is passedaccording to the diffusion tensor data D(l, m, k) when the value of thetime series data of the brain function information of the gray mattervoxel is passed to the white matter voxel as described above. Further, alarger value between the value of the white matter voxel rewritten inthe above-described procedure and the original value of the white mattervoxel may be set as the value of the white matter voxel.

Next, at Step S30, based on the above-described diffusion tensor data:

$\begin{matrix}{{{D\left( {l,m,k} \right)} = \begin{pmatrix}{D_{ll}\left( {l,m,k} \right)} & {D_{lm}\left( {l,m,k} \right)} & {D_{lk}\left( {l,m,k} \right)} \\{D_{lm}\left( {l,m,k} \right)} & {D_{mm}\left( {l,m,k} \right)} & {D_{mk}\left( {l,m,k} \right)} \\{D_{lk}\left( {l,m,k} \right)} & {D_{mk}\left( {l,m,k} \right)} & {D_{kk}\left( {l,m,k} \right)}\end{pmatrix}},} & (9)\end{matrix}$

the inter-voxel connection degree calculation means 4 calculates aconnection degree vector:

{right arrow over (C)}(l,m,k)=(c ₁(l,m,k),c _(m)(l,m,k),c_(k)(l,m,k))^(T)  (10)

between voxels adjacent to each other from, for example,

{right arrow over (C)}(l,m,k)=(1−α){right arrow over (1)}+α{right arrowover (D)}′(l,m,k).  (11)

Here each component of

{right arrow over (D)}′(l,m,k)=(D′ ₁(l,m,k),D′ _(m)(l,m,k),D′_(k)(l,m,k))^(T)  (12)

is defined as, for example,

$\begin{matrix}{{{{\overset{\rightarrow}{D}}_{l}^{\prime}\left( {l,m,k} \right)} = \frac{{D_{l}\left( {l,m,k} \right)} + {D_{l}\left( {{l + 1},m,k} \right)}}{2}},{{{\overset{\rightarrow}{D}}_{m}^{\prime}\left( {l,m,k} \right)} = \frac{{D_{m}\left( {l,m,k} \right)} + {D_{m}\left( {l,{m + 1},k} \right)}}{2}},{{{\overset{\rightarrow}{D}}_{k}^{\prime}\left( {l,m,k} \right)} = {\frac{{D_{m}\left( {l,m,k} \right)} + {D_{m}\left( {l,m,{k + 1}} \right)}}{2}.}}} & (13)\end{matrix}$

This is called an averaged diffusion degree vector between adjacentvoxels hereinafter. Also, “α” is a parameter to satisfy

0≦α≦1,  (14)

and {right arrow over (1)} is a constant vector to satisfy

{right arrow over (1)}=(1,1,1)^(T).  (15)

In Equation (13),

{right arrow over (D)}(l,m,k)=(D _(l)(l,m,k),D _(m)(l,m,k),D_(k)(l,m,k))^(T)  (16)

is a vector to represent a degree of diffusion of protons (this will behereinafter called a diffusion degree vector), wherein a first componentD_(l)(l, m, k) represents a degree of diffusion of protons in the X-axisdirection in the voxel of a position (l, m, k), a second componentD_(m)(l, m, k) represents a degree of diffusion of protons in the Y-axisdirection in the voxel of the position (l, m, k), and a third componentD_(k)(l, m, k) represents a degree of diffusion of protons in the Z-axisdirection in the voxel of the position (l, m, k). Here the X, Y, and Zdirections are assumed to be the same directions as those shown in FIG.7, respectively. Incidentally, it is premised in Equation (9) that thediffusion tensor is a symmetric tensor.

Using the diffusion degree vector {right arrow over (D)}(l,m,k) havingsuch implications, each components C₁(l, m, k), C_(m)(l, m, k), andC_(k)(l, m, k) of the connection degree vector {right arrow over(C)}(l,m,k) between adjacent voxels composed as Equation (11) viaEquation (13) represent a connection degree between the voxel of theposition (l, m, k) and the voxel of the position (l+1, m, k), aconnection degree between the voxel of the position (l, m, k) and thevoxel of the position (l, m+1, k), and a connection degree of the voxelof the position (l, m, k) and the voxel of the position (l, m, k+1),respectively.

Note that in the present embodiment, although a connection degreebetween voxels which are adjacent to each other in an oblique direction(for example, voxel (l, m, k) and voxel (l+1, m+1, k)) is not directlytaken into consideration, it is indirectly taken into consideration viaa voxel in a perpendicular direction (voxel (l+1, m, k) or voxel (l,m+1, k) in this case). Needless to say, it may be configured such thatthe connection degree between the voxels which are adjacent to eachother in the oblique direction is directly taken into consideration.

Further, as the diffusion degree vector {right arrow over (D)}(l,m,k),for example,

(I) in an ellipsoid model,

$\begin{matrix}{{{D_{l}\left( {l,m,k} \right)} = \sqrt{\frac{2{D}}{{{D_{mm}\left( {l,m,k} \right)}{D_{kk}\left( {l,m,k} \right)}} - {D_{mk}\left( {l,m,k} \right)}^{2}}}},{{D_{m}\left( {l,m,k} \right)} = \sqrt{\frac{2{D}}{{{D_{ll}\left( {l,m,k} \right)}{D_{kk}\left( {l,m,k} \right)}} - {D_{lk}\left( {l,m,k} \right)}^{2}}}},{{D_{k}\left( {l,m,k} \right)} = \sqrt{\frac{2{D}}{{{D_{mm}\left( {l,m,k} \right)}{D_{mm}\left( {l,m,k} \right)}} - {D_{lm}\left( {l,m,k} \right)}^{2}}}}} & (17)\end{matrix}$

is employed. Here |D| represents a determinant of a matrix representedwith Equation (9).

In addition, for example,

(II) using diagonal elements D_(ll)(l, m, k), D_(mm)(l, m, k) andD_(kk)(l, m, k) of the matrix represented with Equation (9),

D _(i)(l,m,k)=|D _(ll)(l,m,k)|,D _(m)(l,m,k)=|D _(mm)(l,m,k) and D_(k)(l,m,k)=|D _(kk)(l,m,k)|  (18)

may be employed.

Moreover, for example,

(III) using an index representing a diffusion anisotropy typified byFA(Fractional Anisotropy):

$\begin{matrix}\sqrt{\frac{3}{2} \cdot \frac{\sum\limits_{\alpha = 1}^{3}\left( {\lambda_{\alpha} - D_{AV}} \right)^{2}}{\sum\limits_{\alpha = 1}^{3}\lambda_{\alpha}^{2}}} & (19)\end{matrix}$

and RA(Relational Anisotropy):

$\begin{matrix}{\frac{1}{\sqrt{6}}\sqrt{\frac{\sum\limits_{\alpha = 1}^{3}\left( {\lambda_{\alpha} - D_{AV}} \right)^{2}}{D_{AV}},}} & (20)\end{matrix}$

the diffusion degree vector {right arrow over (D)}(l,m,k) may also bedetermined. Here DAV is

$\begin{matrix}{D_{AV} = {\frac{\sum\limits_{\alpha = 1}^{3}\lambda_{\alpha}}{3}.}} & (21)\end{matrix}$

In addition to these,

(IV) it is also possible that the white matter voxel and the gray mattervoxel of the brain are determined from the predetermined anatomical dataof the brain (T1 weighted image acquired by the MRI device 50) acquiredin advance, and then the degree of diffusion vector of the above (I) isused in the white matter voxel, while the other degree of diffusionvector (for example, the above (II) or (III)) is used in the gray mattervoxel.

As described above, as the diffusion degree vector {right arrow over(D)}(l,m,k) in the present invention, any vectors may be used as far asit is a quantity capable of specifying the running direction of thenerve fiber from the diffusion tensor data D(l, m, k) acquirable fromthe MRI device 50.

In addition, instead of inequality (14) relevant to the parameter α, thevalue of parameter α may be set so as to be switched between the graymatter voxel and the white matter voxel, for example, such that itbecomes a large value in the gray matter voxel and a small value in thewhite matter voxel.

Furthermore, instead of the connection degree vector {right arrow over(C)}(l,m,k) of Equation (11),

{right arrow over (C)}(l,m,k)={right arrow over (1)}+α{right arrow over(D)}′(l,m,k)  (22)

may be used.

Further, in order to emphasize the running direction of the nervefibers, a value obtained by raising each components c_(l)(l, m, k),c_(m)(l, m, k) and c_(k)(l, m, k) of the connection degree vector {rightarrow over (C)}(l,m,k) of Equation (11) or Equation (22) to the N-thpower (N is a natural number of two or more) may be employed.

In addition, values obtained by multiplying values of these componentsby a may be in the white matter voxel, and values obtained bymultiplying values thereof by b may be used in the gray matter voxel.Moreover, only the largest component among these components may beemphasized by the above-described method. Further, only the componentwhich has the value equal to or more than {c_(l)(l, m, k)+c_(m)(l, m,k)+c_(k)(l, m, k)}/3 may be emphasized by the above-described method.

Still further, the connection degree vector {right arrow over(C)}(l,m,k) may be constituted such that the gray matter voxel isconnected to only six adjacent voxels (six directions), and then thewhite matter voxel is connected to only a voxel whose inner product withthe eigenvector corresponding to the maximum eigenvalue of eigenequation (5) is the maximum, and with a voxel having of a point symmetryrelation thereof (two directions).

Next, at Step S40, in order to subject the data illustrated in FIG. 5(A)to a nonparametric regression analysis from the time series data ρ(l, m,k, i) of the brain function information pre-processed at Step S20 andthe connection degree vector {right arrow over (C)}(l,m,k) calculated atStep S40, for example, if there are six adjacent voxels, the dataevaluation value constitution means 5 constitutes a data evaluationvalue Q:

$\begin{matrix}{Q = {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\left\{ {{\varphi (i)} - {\hat{\varphi}(i)}} \right\}^{2}}} + {{\kappa \begin{bmatrix}{{\sum\limits_{l = 1}^{L - 1}{{C_{l}\left( {l,m,k} \right)}\begin{Bmatrix}{{\hat{a}\left( {{l + 1},m,k} \right)} -} \\{\hat{a}\left( {l,m,k} \right)}\end{Bmatrix}^{2}}} +} \\{{\sum\limits_{m = 1}^{M - 1}{C_{m}\left( {l,m,k} \right)\begin{Bmatrix}{{\hat{a}\left( {l,{m + 1},k} \right)} -} \\{\hat{a}\left( {l,m,k} \right)}\end{Bmatrix}^{2}}} +} \\{\sum\limits_{k = 1}^{K - 1}{{C_{k}\left( {l,m,k} \right)}\begin{Bmatrix}{{\hat{a}\left( {l,m,{k + 1}} \right)} -} \\{\hat{a}\left( {l,m,k} \right)}\end{Bmatrix}^{2}}}\end{bmatrix}}.}}} & (23)\end{matrix}$

Here “i” is time, “l”, “m” and “k” are variables representing thepositions of the X direction, the Y direction, and the Z direction ofthe voxel, respectively, is as described in the above. Note that sincethere may be a voxel which does not represent the brain (nerve) amongthe stereoscopically configured voxels shown in FIG. 7 in practice, “l”,“m”, “k” relevant to that area shall be excepted. Meanwhile, φ(i) istime series data of a variable representing CLASS in FIG. 5(A), and hastwo values of 1 (=ON) and 0 (=OFF) in this case. In the nonparametricregression analysis in accordance with the present embodiment, while thetime series data ρ(l, m, k, i) of the brain function information asshown in FIG. 5(A) is an explaining variable, and the time series dataφ(i) of a variable representing CLASS is an explained variable, it isassumed a linear regression equation:

$\begin{matrix}{{\hat{\varphi}(i)} = {\sum\limits_{l = 1}^{L}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{\hat{a}\left( {l,m,k} \right)}{\rho \left( {l,m,k,i} \right)}}}}}} & (24)\end{matrix}$

between these variables in which â(l,m,k) is used as a regressioncoefficient. Here {circumflex over (φ)} and â represent estimationsolutions, respectively, and κ is a weight parameter representingcontinuity between voxels adjacent to each other.

Although Equation (23) constitutes the evaluation value based on thecontinuity between adjacent voxels as described above, the evaluationvalue based on smoothness between adjacent voxels may be constitutedinstead of that.

Grounds using the nonparametric regression analysis as the data analysistechnique in the present embodiment are that the number of explainedvariables φ(i) is extremely small as compared with the number ofexplaining variables ρ(l, m, k, i). In the example shown in FIG. 5(A),there are 64×64×64=262144 explaining variables ρ(l, m, k, i) the same asthe number of voxels, whereas the number of measured values of theexplained variable φ(i) is 18. The number of measured values of theexplained variable is 10 in number in a normal case, and may exceed 100.Since the normal regression analysis cannot be performed in such asituation, the nonparametric regression analysis is used in the presentembodiment (Non-patent Document 6: “Spline Smoothing and NonparametricRegression”, R. L. Eubank, Marcel Dekker, Newyork, 1988).

Usually, in the nonparametric regression analysis, {circumflex over(φ)}(i) which minimizes following evaluation value Q′:

$\begin{matrix}{Q^{\prime} = {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\left\{ {{\varphi (i)} - {\hat{\varphi}(i)}} \right\}^{2}}} + {\kappa {\sum\limits_{i = 1}^{I - 1}\left\{ {{\hat{\varphi}\left( {i + 1} \right)} - {\hat{\varphi}(i)}} \right\}^{2}}}}} & (25)\end{matrix}$

is determined by assuming continuity in the explained variable φ(l, m,k, i). Here the first term of the right-hand side is a term indicatingan error from the estimated solution {circumflex over (φ)}(i) of theexplained variable φ(i) at each time “i”, and the second term thereof isa term for evaluating the continuity to a time direction of theexplained variable φ(i).

However, the continuity cannot be assumed with respect to the value ofthe explained variable φ(i) as can be seen form the example shown inFIG. 5(A). For that reason, the data evaluation value Q represented byEquation (23) is constituted based on an enhanced nonparametricregression analysis so as to impose constraints of such continuity notto the explained variable φ(i) but to the explaining variable ρ(l, m, k,i) (Non-patent Document 7: “Nonparametric regression analysis of brainfunction images”, H. Tukimoto et al., Institute of Electronics,Information and Communication Engineers D-II, Vol. J84, No. 12, pp2623-2633, December, 2001) in the present embodiment.

Here, the meaning of Equation (23) will be described. The first term ofthe right-hand side is a term indicating an error from the estimatedsolution {circumflex over (φ)}(i) of the explained variable φ(i) at eachtime “i”, which is the same as that in Equation (25).

The second term to the fourth term are terms indicating the continuityof effects for the adjacent voxels to have on the explained variableφ(i), and this continuity can be evaluated by effects for adjacentexplaining variables ρ(l, m, k, i) to have on the explained variable,namely, the continuity of the regression coefficient â(l,m,k) ofEquation (24).

Further, in the present embodiment, each components C_(l)(l, m, k),C_(m)(l, m, k), and C_(k)(l, m, k) of the connection degree vector{right arrow over (C)}(l,m,k) calculated by the inter-voxel connectiondegree calculation means 4 are introduced as a weighting factor to thecontinuity between adjacent voxels with respect to the X direction, theY direction and the Z direction, in the second term to the fourth term,respectively.

Since these components are a function of each components D′_(l)(l, m,k), D′_(m)(l, m, k) and D′_(k)(l, m, k) of the averaged diffusion degreevector {right arrow over (D)}′(l,m,k), respectively, as shown inEquation (11), the data evaluation value Q of Equation (23) will resultin reflecting the running direction (diffusion anisotropy of proton) ofthe nerve fiber (neuron).

Next, at Step S50, the data analysis means 6 performs calculation fordetermining the extremum of the data evaluation value Q constituted atStep S40. A regression coefficient â(l,m,k) which minimizes the dataevaluation value Q of Equation (23) is calculated in the presentembodiment.

For the purpose of simplification, a data evaluation value Q″ obtainedby rewriting Equation (23) will be hare described as

$\begin{matrix}{Q^{''} = {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\left\{ {{\varphi (i)} - {\hat{\varphi}(i)}} \right\}^{2}}} + {\kappa {\sum\limits_{n = 1}^{L^{3} - 1}{{C(n)}\left\{ {{\hat{a}\left( n^{\prime} \right)} - {\hat{a}(n)}} \right\}^{2}}}}}} & (26)\end{matrix}$

where “n” is a positive discrete variable representing a position of avoxel that is one-dimensionally re-arranged by assigning a predeterminednumber to all the stereoscopically configured voxels, and has aone-to-one correspondence with three display variables (l, m, k).Further, since it is set as L=M=K for the purpose of simplification, thetotal number of voxels is L³. Meanwhile, C (n) is a coefficientrepresenting a connection degree between a voxel n and a voxel n′adjacent to the voxel n when all the voxels are one-dimensionallyrearranged in this way, and this coefficient is uniquely determined fromthe connection vector {right arrow over (C)}(l,m,k) of Equation (11).

For example, when the discussion is limited to the two-dimensional sliceimage S_(k) shown in FIG. 3(B), voxels adjacent to a voxel of n=130 arefour of n=127, 129, 131, 257. According to the connection degree vector{right arrow over (C)}(l,m,k) calculated at Step S30, a connectiondegree between the voxel n=127 and the voxel n=130, a connection degreebetween the voxel n=129 and the voxel n=130, a connection degree betweenthe voxel n=131 and the voxel n=130, and a connection degree between thevoxel n=257 and the voxel n=130 are determined as, for example, 2, 2, 5,and 5, respectively.

Namely, in this case,

2{â(127)−â(130)}²,2{â(129)−â(130)}²,5{â(131)−â(130)}²,5{â(257)−â(130)}²

are obtained.

At this time, using a least square method, a regression coefficient â(n)can be determined as

â=(X^(T)X+Iκ_(O)C)⁻¹X^(T){right arrow over (φ)}  (27)

where X is a L³×I matrix X having a component of ρ(n, i), {right arrowover (φ)} is an I-dimensional vector having a component of φ(i), {rightarrow over (a)} is an L³-dimensional vector having a component of â(n),κ_(O) is the optimum value of κ determined by a Cross Validation method,namely, κ which minimizes

$\begin{matrix}{{CV} = {\frac{1}{I}{\sum\limits_{i = 1}^{I}{\left\{ {{\varphi (i)} - {\hat{\varphi}}_{\lbrack i\rbrack}} \right\}^{2}.}}}} & (28)\end{matrix}$

Here {circumflex over (φ)}_([i]) is a value obtained by calculating aregression coefficient except for i-th case to thereby predict ameasured value of i-th case using the regression coefficient.

Next, at Step S60, the image generation means 7 generates images towhich a color tone such that a voxel whose regression coefficient valueis positive (namely, positive correlation) is set to a color tone in awhite direction, and a voxel whose regression coefficient value isnegative (namely, negative correlation) is set to a color tone in ablack direction, for example, using a voxel whose regression coefficientvalue is 0 (namely, no correlation) as a standard of a grayscale isapplied, based on the regression coefficient â(n) calculated as above.

In addition to that, values of top 5% or more in a frequency histogramof the voxel values which can be taken on the grayscale may bedisplayed, or full color images may be generated in accordance with apredetermined rule.

Subsequently, at Step S70, the image display means 8 stereoscopicallydisplays the images generated as described above.

As described above, in the first embodiment, in addition to theevaluation term of the error based on the time series data of the brainfunction information acquired from the MRI device, the evaluation value,to which the evaluation term of the continuity in the space directionbased on the diffusion tensor data acquired also from the MRI device isadded, is constituted. The evaluation term has been subjected to thenonparametric linear regression analysis and the resultant regressioncoefficient has been set to a target of the image display, thus allowingactivated brain regions to be located in consideration of the connectionstructure between the activated brain regions as well.

Incidentally, although the process of the preprocessing (d) is providedat Step S20 other than the normal preprocessing (a) to (c) in thepresent embodiment, the preprocessing (d) may be removed. This issimilar also to subsequent embodiments.

Incidentally, although Equation (23) is constituted as the dataevaluation value in the first embodiment, various kinds of evaluationvalues can be constituted depending on the data analysis technique inachieving the object of the present invention. Hence, while severalmodified examples of the above first embodiment will be then described,only portions different from those of the first embodiment will behereinafter described in order to avoid duplication.

Modified Example 1 of First Embodiment

In the first embodiment, the data analysis means 6 performs thenonparametric regression analysis by using the linear regressionequation of Equation (19) at Step S50, but more strictly, it is alsoconsidered that the regression expression is performed with generaln-dimensional functions, such as a quadratic function, a cubic function,or the like. Upon performing such a nonlinear regression analysis,Neural Network models can be used. As a typical Neural Network model,there is a hierarchical type Neural Network model composed of an inputlayer, a middle layer, and an output layer.

In the present modified embodiment, at Step S40 the data evaluationvalue constitution means 5 constitutes an evaluation value, in which anevaluation value for evaluating the continuity between adjacent voxelsbased on the connection degree vector {right arrow over (C)}(l,m,k)calculated by the inter-voxel connection degree calculation means 4 isadded to a term of a square error which is an evaluation value of normallearning in the Neural Network model.

Subsequently, after the data analysis means 6 performs an analysis atStep S50 using an Error Back Propagation Method or the like in order todetermine the minimum value of the evaluation value thus constituted,the image generation means 7 generates at Step S60 sensitivity (effectshaving on an output when an input is changed) between the input (timeseries data of the brain function information) and the output as thedisplay image in a manner similar to that of the first embodiment.Thereafter, at Step S70, the image display means 8 stereoscopicallydisplays the images generated as described above.

This makes it possible to obtain effects similar to those of the firstembodiment.

Modified Example 2 of First Embodiment

The data evaluation value constitution means 5 constitutes at Step S40the evaluation value Q of Equation (23) using the time series data ρ(l,m, k, i) of the brain function as the explaining variable, and φ(i) asthe explained variable in the first embodiment, whereas, in the presentmodified embodiment, an evaluation value Q′″, for example,

$\begin{matrix}{Q^{\prime\prime\prime} = {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\begin{Bmatrix}{{\rho \left( {l,m,k,i} \right)} -} \\{\hat{\rho}\left( {l,m,k,i} \right)}\end{Bmatrix}^{2}}} + {\frac{\kappa}{I}{\sum\limits_{i = 1}^{I}\begin{bmatrix}{{{C_{l}\left( {l,m,k} \right)}\begin{Bmatrix}{{\hat{\rho}\left( {{l + 1},m,k,i} \right)} -} \\{\hat{\rho}\left( {l,m,k,i} \right)}\end{Bmatrix}^{2}} +} \\{{{C_{m}\left( {l,m,k} \right)}\begin{Bmatrix}{{\hat{\rho}\left( {l,{m + 1},k,i} \right)} -} \\{\hat{\rho}\left( {l,m,k,i} \right)}\end{Bmatrix}^{2}} +} \\{{C_{k}\left( {l,m,k} \right)}\begin{Bmatrix}{{\hat{\rho}\left( {l,m,{k + 1},i} \right)} -} \\{\hat{\rho}\left( {l,m,k,i} \right)}\end{Bmatrix}^{2}}\end{bmatrix}}}}} & (29)\end{matrix}$

is constituted for every voxel (l, m, k) by using the reverserelationship, namely, using φ(i) as the explaining variable, and ρ(l, m,k, i) as the explained variable. Here the following constraintcondition:

$\begin{matrix}{{\hat{\rho}\left( {l,m,k,i} \right)} = {{\sum\limits_{l = 1}^{L}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{\hat{a}\left( {l,m,k} \right)}{\varphi (i)}}}}} + b}} & (30)\end{matrix}$

is given.

Subsequently, at Step S50 the data analysis means 6 determines anoptimal κ using the Cross Validation method in order to determine theminimum value of the evaluation value Q′″ constituted as describedabove, and determines a regression coefficient â(l,m,k) using aprobabilistic search algorithm, such as GA (Genetic Algorithm).

Next, at Step S60, the image generation means 7 generates a value basedon the brain function data ρ(l, m, k) of the voxel, which is determinedto be significant upon variously testing the above-described analysisresults, as the display image. Thereafter, at Step S70, the imagedisplay means 8 stereoscopically displays the images generated asdescribed above.

This makes it possible to obtain effects similar to those of the firstembodiment.

Modified Example 3 of First Embodiment

The data evaluation value constitution means 5 constitutes at Step S40the evaluation value of Equation (23) using the time series data ρ(l, m,k, i) of the brain function as the explaining variable, and φ(i) as theexplained variable in the modified example 1 of the first embodiment,whereas, in the present modified embodiment, the nonlinear regressionanalysis can also be performed using the hierarchical type NeuralNetwork model, by using the reverse relationship, namely using φ(i) asthe explaining variable, and ρ(l, m, k, i) as the explained variable.Note that since the Error Back Propagation Method cannot be used upondetermining the extremum at Step S50 in this case, the data analysismeans 6 determines the optimal κ using the Cross Validation methodinstead, and performs the data analysis by the probabilistic searchalgorithm, such as GA. Subsequently, at Step S60, the image generationmeans 7 generates a value based on the brain function data ρ(l, m, k) ofthe voxel, which is determined to be significant upon variously testingthe above-described analysis results, as the display image. Thereafter,at Step S70, the image display means 8 stereoscopically displays theimages generated as described above.

This makes it possible to obtain effects similar to those of the firstembodiment.

Modified Example 4 of First Embodiment

While the data analysis means 6 performs the nonparametric regressionanalysis by using the linear regression equation of Equation (24) atStep S50 in the first embodiment, classificational analysis techniques,such as, a discriminant analysis (Non-patent Document 8: “Story ofmultivariate analysis”, T. Arima, S. Ishimura, Tokyo Tosho, 1987), aquantification II (Non-patent Document 9), a decision tree (Non-patentDocument 10: “Data analysis by A.I.”, J. R. Quinlan, Toppan, 1995), asupport vector machine (Non-patent Document 11: “Introduction to supportvector machine”, Nello Cristianini and John Shawe-Taylor, Kyoritsushuppan, 2005) or the like may also be used as the data analysistechnique.

In any case, data classification is performed using various kinds ofdiscrimination functions, decision trees, or the like, while using theexplaining variable as the time series data ρ(l, m, k, i) of the brainfunction information, and the external criterion as the time series dataof the task and the rest, for example. In the present modifiedembodiment, the data evaluation value constitution means 5 constitutesat Step S40 an evaluation value in which the connection degree vector{right arrow over (C)}(l,m,k) calculated at Step S30 is incorporatedinto a normal evaluation value in each analysis techniques in that case.

Subsequently, at Step S50, the data analysis means 6 then performs thedata analysis using each analysis technique. If the evaluation value ischanged as described above, the normal solution method may not be used,and thus the data analysis means 6 may determine the optimal κ using theCross Validation method, and perform the data analysis by theprobabilistic search algorithm, such as GA in that case.

Subsequently, at Step S60, the image generation means 7 generates avalue based on the brain function data ρ(l, m, k) of the voxel, which isdetermined to be significant upon variously testing the classificationdata obtained at Step S50, as the display image, and the image displaymeans 8 stereoscopically displays at Step S70 the images generated asdescribed above.

This makes it possible to obtain effects similar to those of the firstembodiment.

Modified Example 5 of First Embodiment

While the data analysis means 6 performs the nonparametric regressionanalysis by using the linear regression equation of Equation (24) atStep S50 in the first embodiment, ICA (Independent Component Analysis)can also be used as the data analysis technique (Non-patent Document 12:“Detailed explanation of Independent Component Analysis Novel world ofsignal analysis”, Aapo Hyvarinen et al., Tokyo Denki University Press2005).

In the present modified embodiment, the data evaluation valueconstitution means 5 constitute at Step S40 an evaluation value based onthe connection degree vector {right arrow over (C)}(l,m,k) calculated bythe inter-voxel connection degree calculation means 4.

Conditions of “spatial independence” in the analysis of the time seriesdata ρ(l, m, k, i) of the brain function information according to theindependent component analysis is changed to “spatial independence inconsideration of the connection degree based on the diffusion tensorinformation” in the present modified embodiment. There are severalspecific techniques of the independent component analysis, but in thecase of, for example, a technique to minimize a cross-correlation, thecross-correlation is not minimized, but is moved closer to theevaluation value based on the connection degree, in the present modifiedembodiment. As a result of this, the cross-correlation between thevoxels with a large connection degree becomes a “degree of independence”which is close to the evaluation value based on the connection degree,while the cross-correlation is minimized between the voxels with a smallconnection degree (or there is no connection degree). More specifically,for example, a weighting coefficient according to the evaluation valuebased on the connection degree is set to each term of the square sum inthe evaluation function which minimizes the square sum of thenon-diagonal elements of the correlation matrix. This weightingcoefficient is constituted so that the larger the connection degreebecomes, the smaller it becomes.

Next, the data analysis means 6 performs the independent componentanalysis at Step S50 in consideration of the evaluation valueconstituted as described above. In that case, the optimal κ isdetermined using the Cross Validation method, and the data analysis isperformed by the probabilistic search algorithm, such as GA.

Next, the image generation means 7 generates at Step S60 independentcomponents obtained as described above as the display image. However,since there are many independent components obtained as the results ofthe above-described analysis, processing of extracting only a row vectorhaving the highest correlation with the task/rest among all the rowvectors is performed.

Thereafter, at Step S70, the image display means 8 stereoscopicallydisplays the images generated as described above.

This makes it possible to obtain effects similar to those of the firstembodiment.

Note that while there are a principal component analysis, a factoranalysis, a quantification III, and the like, as a technique ofextracting predetermined main components from the time series data ofthe brain function information other than the above independentcomponent analysis, these analysis techniques can fundamentally performthe analysis in a manner similar to that described above as well.

Second Embodiment

FIG. 10 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a second embodiment ofthe present invention. A brain function analysis apparatus 20 of thesecond embodiment has a configuration provided with inter-voxelconnection degree calculation means 24 instead of the inter-voxelconnection degree calculation means 4, the data evaluation valueconstitution means 5, and the data analysis means 6 in the brainfunction analysis apparatus 10 of the first embodiment, data evaluationvalue constitution means 25, data smoothing means 200, and data analysismeans 26. The same symbol is given to the same configuration as that ofthe first embodiment, and repeated descriptions thereof will be omittedhereinafter. Note that although the brain function analysis apparatus 20employs a display console type in which major constitution means 20A ofthe present embodiment, the image display means 8, and the memory means9 are integrated in the present embodiment, there may be employed such aconfiguration that the image display means 8, or the image display means8 and the memory means 9 are separated from the major constitution means20A as respectively independent image display unit and storage unit.

After calculating the averaged diffusion degree vector {right arrow over(D)}′(l,m,k) (Equation (12)) from the diffusion tensor data D(l, m, k)(Equation (9)) acquired by the diffusion tensor data acquisition means 2in a manner described in detail in the first embodiment, the inter-voxelconnection degree calculation means 24 calculates the connection degreevector {right arrow over (W)}(l,m,k) for determining smoothness betweenadjacent voxels upon smoothing the time series data ρ(l, m, k, i) of thebrain function information which is pre-processed by the datapreprocessing means 3 according to a size of each components D′_(l)(l,in, k), D′_(m)(l, m, k) and D′_(k)(l, m, k) of the averaged diffusiondegree vector {right arrow over (D)}′(l,m,k).

The data smoothing means 200 constitutes a weighted average filter forsmoothing the time series data ρ(l, m, k, i) of the brain functioninformation which is pre-processed by the data preprocessing means 3,using the connection degree vector {right arrow over (W)}(l,m,k)calculated by the inter-voxel connection degree calculation means 24,and smoothes the time series data ρ(l, m, k, i) of the brain functioninformation which is pre-processed by the data preprocessing means 3using the weighted average filter.

The data analysis means 26 performs data analysis on the data smoothedby the data smoothing means 200 using techniques, such as SPM or thelike.

FIG. 11 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus 20 in accordance withthe second embodiment shown in FIG. 10.

At Step S100, the brain function data acquisition means 1 acquires theoriginal time series data ρ′(l, m, k, i) of the brain functioninformation measured by the MRI device 50. At the same step, thediffusion tensor information acquisition means 2 acquires the diffusiontensor data D(l, m, k) measured also by the MRI device 50.

Next, at Step S110, the data preprocessing means 3 performs thepreprocessing (a) to (d) explained in full detail in the firstembodiment on the original time series data ρ′(l, m, k, i) of the brainfunction information acquired at Step S100 to thereby generate the timeseries data ρ(l, m, k, i) of the brain function information.

Next, at Step S120, the inter-voxel connection degree calculation means24 first calculates the averaged diffusion degree vector {right arrowover (D)}′(l,m,k) from the diffusion tensor data D(l, m, k) which isacquired at Step S100 in a manner explained in full detail in the firstembodiment.

Subsequently, the connection degree vector {right arrow over (W)}(l,m,k)for determining smoothness between adjacent voxels upon smoothing thetime series data ρ(l, m, k, i) of the brain function information whichis pre-processed by the data preprocessing means 3 according to the sizeof each components D′_(l)(l, m, k), D′_(m)(l, m, k) and D′_(k)(l, m, k)of the averaged diffusion degree vector {right arrow over (D)}′(l,m,k)is calculated as, for example,

$\begin{matrix}\begin{matrix}{{\overset{\rightarrow}{W}\left( {l,m,k} \right)} = \left( {{w_{l}\left( {l,m,k} \right)},{w_{m}\left( {l,m,k} \right)},{w_{k}\left( {l,m,k} \right)}} \right)} \\{\equiv {\begin{pmatrix}{\frac{w\; {D_{l}^{\prime}\left( {l,m,k} \right)}}{\sum\limits_{{\alpha = l},m,k}{D_{\alpha}^{\prime}\left( {l,m,k} \right)}},} \\{\frac{w\; {D_{m}^{\prime}\left( {l,m,k} \right)}}{\sum\limits_{{\alpha = l},m,k}{D_{\alpha}^{\prime}\left( {l,m,k} \right)}},} \\\frac{w\; {D_{k}^{\prime}\left( {l,m,k} \right)}}{\sum\limits_{{\alpha = l},m,k}{D_{\alpha}^{\prime}\left( {l,m,k} \right)}}\end{pmatrix}.}}\end{matrix} & (31)\end{matrix}$

Next, at Step S130, the data smoothing means 26 anisotropically smoothesthe time series data ρ(l, m, k, i) of the brain function informationwhich is pre-processed at Step S110 using the connection degree vector{right arrow over (W)}(l,m,k) calculated at Step S120, for example, as

$\begin{matrix}{{\overset{\_}{\rho}\left( {l,m,k,i} \right)} = {{\frac{1}{w + W}\begin{bmatrix}\begin{matrix}\left\{ {{W\; \rho \left( {l,m,k,i} \right)} +} \right. \\{{w_{l} \cdot \begin{Bmatrix}{{\rho \left( {{l - 1},m,k,i} \right)} +} \\{\rho \left( {{l + 1},m,k,i} \right)}\end{Bmatrix}} +}\end{matrix} \\{{w_{m} \cdot \begin{Bmatrix}{{\rho \left( {l,{m - 1},k,i} \right)} +} \\{\rho \left( {l,{m + 1},k,i} \right)}\end{Bmatrix}} +} \\{w_{k} \cdot \begin{Bmatrix}{{\rho \left( {l,m,{k - 1},i} \right)} +} \\{\rho \left( {l,m,{k + 1},i} \right)}\end{Bmatrix}}\end{bmatrix}}.}} & (32)\end{matrix}$

Here “w” and “W” are weight factors, and they are normalized so as tosatisfy

$\begin{matrix}{{\left\{ {\sum\limits_{{\alpha = l},m,k}{D_{\alpha}^{\prime}\left( {l,m,k} \right)}} \right\} + W} = {{w + W} = 1.}} & (33)\end{matrix}$

FIG. 12 illustrates a data smoothing technique in accordance with thepresent embodiment, and shows the time series data ρ_(k)(l, m, i) of thebrain function information on nine voxels about a position (l, m) of acertain two-dimensional slice image S_(k). In this example, ρ_(k)(l, m,i)=5, ρ_(k)(l−1, m, i)=ρ_(k)(l−1, m−1, i)=ρ_(k)(l, m−1, i)=ρ_(k)(l, m+1,i)=ρ_(k)(l+1, m, i)=2 and ρ_(k)(l+1, m−1, i)=ρ_(k)(l−1, m+1,i)=ρ_(k)(l+1, m+1, i)=3, where four of ρ_(k)(l, m−1, i), ρ_(k)(l, m+1,i), ρ_(k)(l−1, m, i), ρ_(k)(l+1, m, i) are used for smoothing ρ_(k)(l,m, i), among them. Further, it is assumed a situation to satisfyD′_(k)(l, m, k)=0 (namely, there is no diffusion of the proton in the Zdirection), D′_(l)(l, m, k)=0.8, D′_(m)(l, m, k)=0.2, w=0.4 and W=0.6.At this time, the connection degree vector {right arrow over (W)}(l,m,k)results in

{right arrow over (W)}(l,m,k)=(0.4×0.8,0.4×0.2,0)=(0.32,0.08,0)  (34)

from Equation (31), so that it is seen that the time series data ρ(l, m,k, i)=5 of the brain function information on the voxel of the position(l, m, k) is smoothed as

$\begin{matrix}{{\overset{\_}{\rho}\left( {l,m,k,i} \right)} = {{\frac{1}{1}\left( {{0.6 \times 5} + {0.32 \times 2 \times 2} + {0.08 \times 2 \times 2}} \right)} = 4.44}} & (35)\end{matrix}$

from Equation (36).

This processing is performed on the time series data ρ(l, m, k, i) ofthe brain function information on all the voxels of the two-dimensionalslice image S_(k), so that it can smooth the data so that a contributionfrom a direction having the largest component (X direction in thisexample) among directions (namely, the components D′_(l)(l, m, k),D′_(m)(l m, k) and D′_(k)(l, m, k)) of the averaged diffusion degreevector {right arrow over (D)}′(l,m,k)) along the running direction ofthe nerve fibers may become large.

Furthermore, as described in a part of Equation (5), it may be soothedonly with two voxels which match with a direction of the eigenvector{right arrow over (v)}_(M)(l,m,k) corresponding to the maximumeigenvalue λ_(M) other than this.

Next, at Step S140, the data analysis means 26 performs data analysis onthe time series data of the brain function information ρ(l,m,k,i)smoothed at Step S140 using analysis techniques, such as SPM or thelike.

Next, at Step S150, the image generation means 7 generates color toneimages, such as grayscale or full color images, with respect to theresults analyzed by the data analysis means 26.

Thereafter, at Step S160, the image display means 8 stereoscopicallydisplays the images generated as described above.

As described above, in the second embodiment, the time series data ofthe brain function information acquired from the MRI device has beensubjected to the smoothing processing based on the connection degreevector calculated from the diffusion tensor data acquired also from theMRI device, so that activated brain regions can be located also inconsideration of the connection structure between the activated brainregions.

Incidentally, the connection degree vector {right arrow over (W)}(l,m,k)of Equation (31) or the algorithm of the smoothing processing ofEquation (32) is just one example, and the other embodiments are alsopossible.

Further, it is clear from the above description that the technique ofthe data smoothing processing in the present embodiment is not dependenton a specific data analysis technique.

Modified Example of Second Embodiment

Although the data analysis is performed using the analysis techniques,such as SPM or the like at Step S140 after performing the smoothingprocessing on the time series data ρ(l, m, k, i) of the brain functioninformation using the connection degree vector {right arrow over(W)}(l,m,k) calculated from the diffusion tensor data D(l, m, k) at StepS130 in the second embodiment, a technique of reversing these steps isalso considered in achieving the object of the present invention.

Accordingly, a modified example of the above second embodiment will bethen described. Incidentally, only portions different from those of thesecond embodiment will be hereinafter described in order to avoidduplication.

FIG. 13 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with the modified example ofthe second embodiment. Although a configuration of a brain functionanalysis apparatus 20′ in accordance with the present modifiedembodiment is fundamentally the same as that of the second embodiment,it differs in that the data smoothing means 200 and the data analysismeans 26 are exchanged. Thus, the data analysis procedure performed bythe brain function analysis apparatus 20′ differs from that of thesecond embodiment. Note that although the brain function analysisapparatus 20′ employs a display console type in which major constitutionmeans 20A′ of the present embodiment, the image display means 8, and thememory means 9 are integrated in the present embodiment, there may beemployed such a configuration that the image display means 8, or theimage display means 8 and the memory means 9 are separated from themajor constitution means 20A′ as respectively independent image displayunit and storage unit.

FIG. 14 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus 20′ shown in FIG. 13.In the present modified embodiment, data analysis means 26′ firstperforms at Step S121 a data analysis on the time series data ρ(l, in,k, i) of the brain function information pre-processed by the datapreprocessing means 3 using the analysis techniques, such as SPM or thelike. Next, at Step S131, the inter-voxel connection degree calculationmeans 24 calculates a connection degree vector {right arrow over(W)}(l,m,k) as shown by Equation (31) in a manner similar to that of thesecond embodiment.

Subsequently, at Step S141, data smoothing means 200′ performs thesmoothing processing on the brain function data ρ(l, m, k) which is theresult of being analyzed by the data analysis means 26′ using Equation(32) in a manner similar to that of the second embodiment. Next, at StepS150, the image generation means 7 generates color tone images, such asgrayscale or full color images according to a predetermined processingrule with respect to the data ρ(l,m,k) after the smoothing processingobtained as described above.

Thereafter, at Step S160, the image display means 8 stereoscopicallydisplays the images generated as described above.

Thus, effects similar to those of the second embodiment are obtainedalso in the present modified embodiment.

Third Embodiment

FIG. 15 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a third embodiment of thepresent invention. A brain function analysis apparatus 30 of the thirdembodiment has a configuration provided with inter-voxel connectiondegree calculation means 34 instead of the inter-voxel connection degreecalculation means 4, the data evaluation value constitution means 5, andthe data analysis means 6 in the brain function analysis apparatus 10 ofthe first embodiment, the clustering means 300, and data analysis means36. The same symbol is given to the same configuration as that of thefirst embodiment, and repeated descriptions thereof will be omittedhereinafter. Note that although the brain function analysis apparatus 30employs a display console type in which major constitution means 30A ofthe present embodiment, the image display means 8, and the memory means9 are integrated in the present embodiment, there may be employed such aconfiguration that the image display means 8, or the image display means8 and the memory means 9 are separated from the major constitution means30A as respectively independent image display unit and storage unit.

After calculating the averaged diffusion degree vector {right arrow over(D)}′(l,m,k) (Equation (12)) from the diffusion tensor data D(l, m, k)(Equation (9)) acquired by the diffusion tensor data acquisition means 2in a manner described in detail in the first embodiment, the inter-voxelconnection degree calculation means 34 calculates a connection degreevector {right arrow over (S)}(l,m,k) as an index for clustering thevoxels according to the size of each components D′_(l)(l, m, k),D′_(m)(l, m, k), and D′_(k)(l, m, k) of the averaged diffusion degreevector {right arrow over (D)}′(l,m,k).

The clustering means 300 clusters the voxels using the connection degreevector {right arrow over (S)}(l,m,k) calculated by the inter-voxelconnection degree calculation means 34.

The data analysis means 36 performs data analysis on the time seriesdata ρ(l, m, k, i) of the brain function information pre-processed bythe data preprocessing means 3 using the analysis techniques, such asSPM or the like, while using the voxel group (cluster) clustered by theclustering means 300 as a unit.

FIG. 16 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus 30 in accordance withthe third embodiment shown in FIG. 15.

At Step S200, the brain function data acquisition means 1 acquires theoriginal time series data ρ′(l, m, k, i) of the brain functioninformation measured by the MRI device 50. At the same step, thediffusion tensor information acquisition means 2 acquires the diffusiontensor data D(l, m, k) measured also by the MRI device 50.

Next, at Step S210, the data preprocessing means 3 performs thepreprocessing (a) to (d) explained in full detail in the firstembodiment on the original time series data ρ′(l, m, k, i) of the brainfunction information acquired at Step S200 to thereby generate the timeseries data ρ(l, m, k, i) of the brain function information.

Next, at Step S220, the inter-voxel connection degree means calculationmeans 34 first calculates the averaged diffusion degree vector {rightarrow over (D)}′(l,m,k) from the diffusion tensor data D(l, m, k) whichis acquired at Step S200 in a manner explained in full detail in thefirst embodiment.

Subsequently, it calculates a connection degree vector {right arrow over(S)}(l,m,k) which is an index for clustering the voxels according to thesize of each components D′_(l)(l, m, k), D′_(m)(l, m, k), and D′_(k)(l,m, k) of the averaged diffusion degree vector {right arrow over(D)}′(l,m,k) as, for example,

{right arrow over (S)}(l,m,k)=(S _(l)(l,m,k),S _(m)(l,m,k),S_(k)(l,m,k))={right arrow over (D)}′(l,m,k)−{right arrow over(h)}(l,m,k)  (36)

where {right arrow over (h)} is a constant vector, representing athreshold of the clustering, written as

{right arrow over (h)}(l,m,k)=(h _(x) ,h _(y) ,h _(z)).  (37)

Next, at Step S230, the clustering means 300 clusters voxels in whichvalues of each components S_(l)(l, m, k), S_(m)(l, m, k), and S_(k)(l,m, k) of the connection degree vector {right arrow over (S)}(l,m,k)calculated at Step S220 become positive in the X direction, the Ydirection and the Z direction, respectively.

FIG. 17 is a view illustrating a clustering technique performed by theclustering means 300 shown in FIG. 15, and shows 16 voxels of a certaintwo-dimensional slice image S_(k). Values of arrows between adjacentvoxels in FIG. 17(A) represent values of the average degree of diffusionvector in the X direction or the Y direction. If the values of eachcomponents of the constant vector {right arrow over (h)} of Equation(37) are set to h_(l)=h_(m)=3 and h_(k)=0, respectively, each values ofthe components S_(l)(l, m, k) and S_(m)(l, m, k) of the connectiondegree vector {right arrow over (S)}(l,m,k) become as shown in FIG.17(B).

At this time, by sequentially connecting adjacent voxels in which thecomponents S_(l)(l, m, k) and S_(m)(l, m, k) of the connection degreevector {right arrow over (S)}(l,m,k) are both positive in eachdirections of the X direction and the Y direction, voxels enclosed withdotted lines will be eventually formed as one cluster.

Since the connection degree vector {right arrow over (S)}(l,m,k) becomesa function of the average degree of diffusion vector D′(l, m, k), suchclustering shall reflect the running direction of the nerve fiber.

Next, at Step S240, the data analysis means 36 performs data analysis onthe time series data ρ(l, m, k, i) of the brain function informationpre-processed at Step S210 using analysis methods, such as SPM or thelike, for every voxel group (cluster) clustered at Step S230. In thatcase, for example, processing of averaging the time series data of thebrain function information within one cluster or the like is required.

Next, at Step S250, the image generation means 7 generates color toneimages, such as grayscale or full color images according to apredetermined processing rule with respect to the data {tilde over(ρ)}(l,m,k) after the date analysis performed at Step S240.

Thereafter, at Step S260, the image display means 8 stereoscopicallydisplays the images generated at Step 250.

As described above, in the third embodiment, the time series data of thebrain function information acquired also from the MRI device has beenanalyzed for every cluster after clustering the voxels based on thediffusion tensor data acquired from the MRI device, so that activatedbrain regions can be located also in consideration of the connectionstructure between the activated brain regions.

Incidentally, the connection degree vector {right arrow over (S)}(l,m,k)of Equation (36) is just one example, and the other embodiments are alsopossible.

Additionally, it may be clustered only with two voxels which match withthe direction of the eigenvector {right arrow over (v)}_(M)(l,m,k)corresponding to the maximum eigenvalue λ_(M), as described in a part ofEquation (5).

Further, it is clear from the above description that the technique ofthe clustering processing in the present embodiment is not dependent ona specific data analysis technique.

Modified Example 1 of Third Embodiment

Although the data analysis is performed on the time series data ρ(l, in,k, i) of the brain function information for every cluster using theanalysis techniques, such as SPM or the like at Step S240 afterclustering the voxels using the connection degree vector {right arrowover (S)}(l,m,k) calculated from the diffusion tensor data D(l, m, k) atStep S230 in the third embodiment, a technique of reversing these stepsis also considered in achieving the object of the present invention.

Accordingly, a modified example of the above third embodiment will bethen described. Incidentally, only portions different from those of thethird embodiment will be hereinafter described in order to avoidduplication.

FIG. 18 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with the modified example ofthe third embodiment. Although a configuration of a brain functionanalysis apparatus 30′ in accordance with the present modifiedembodiment is the same as that of the third embodiment basically, itdiffers in that the clustering means 300 and the data analysis means 36are exchanged. Thus, the data analysis procedure performed by the brainfunction analysis apparatus 30′ differs from that of the thirdembodiment. Incidentally, although the brain function analysis apparatus30′ employs a display console type in which major constitution means30A′ of the present embodiment, the image display means 8, and thememory means 9 are integrated in the present embodiment, there may beemployed such a configuration that the image display means 8, or theimage display means 8 and the memory means 9 are separated from themajor constitution means 30A′ as respectively independent image displayunit and storage unit.

FIG. 19 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus 30′ shown in FIG. 18.In the present modified embodiment, at Step S221, data analysis means36′ first performs a data analysis on the time series data ρ(l, m, k, i)of the brain function information pre-processed by the datapreprocessing means 3 using the analysis techniques, such as SPM or thelike. Next, at Step S231, the inter-voxel connection degree calculationmeans 34 calculates a connection degree vector {right arrow over(S)}(l,m,k) as shown by Equation (36) in a manner similar to that of thethird embodiment.

Next, at Step S241, clustering means 300′ clusters voxels as shown inFIG. 17 with respect to the brain function data ρ(l, m, k) analyzed bythe data analysis means 36′ in a manner similar to that of the thirdembodiment. Next, at Step S250, the image generation means 7 generatescolor tone images, such as grayscale or full color images according to apredetermined processing rule with respect to the brain function data{tilde over (ρ)}(l,m,k) clustered as described above.

Thereafter, at Step S260, the image display means 8 stereoscopicallydisplays the images generated as described above.

Thus, effects similar to those of the third embodiment are obtained alsoin the present modified embodiment.

Modified Example 2 of Third Embodiment

After performing a data analysis on the time series data ρ(l, m, k, i)of the brain function information pre-processed by the datapreprocessing means 3 using the analysis techniques, such as SPM or thelike, the brain function data ρ(l, m, k) after the data analysis isclustered using the connection degree vector {right arrow over(S)}(l,m,k) calculated by the inter-voxel connection degree calculationmeans 34 in the modified example 1 of the third embodiment, but it isalso considered to use a classificational analysis technique as the dataanalysis technique.

Accordingly, a case where a decision tree which is one of theclassificational analysis techniques is used as the data analysistechnique will be described as one example in the present modifiedembodiment. For example, when the decision tree technique is used whileusing the explaining variable (attribute) as the time series data ρ(l,m, k) of the brain function information, and the external criterion(task) as the time series data of the task and the rest as shown in FIG.4, unnecessary attributes (voxels) are removed and only requiredattributes (voxels) remain. Thus, the voxels remained without beingremoved are clustered using the connection degree vector {right arrowover (S)}(l,m,k).

For example, (1) a certain voxel is chosen, (2) voxels with connectiondegree not less than a constant value around the voxel are clustered,(3) the processing (2) is continued until there is no voxel withconnection degree not less than the constant value around it, and (4) ifthere are still any voxels remained, a certain voxel is chosen and theprocess will be returned to the processing (2).

Next, at Step S250, the image generation means 7 generates color toneimages, such as grayscale or full color images according to apredetermined processing rule with respect to the brain function data{tilde over (ρ)}(l,m,k) clustered as described above.

Thereafter, at Step S260, the image display means 8 stereoscopicallydisplays the images generated as described above.

Thus, effects similar to those of the third embodiment are obtained alsoin the present modified embodiment.

Fourth Embodiment

FIG. 20 is a block diagram showing a schematic configuration of a brainfunction analysis apparatus in accordance with a fourth embodiment. Abrain function analysis apparatus 40 of the fourth embodiment has aconfiguration provided with inter-voxel connection degree calculationmeans 44 instead of the inter-voxel connection degree calculation means4, the data evaluation value constitution means 5, and the data analysismeans 6 in the brain function analysis apparatus 10 of the firstembodiment, and data test means 400. The same symbol is given to thesame configuration as that of the first embodiment, and repeateddescriptions thereof will be omitted hereinafter. Incidentally, althoughthe brain function analysis apparatus 40 employs a display console typein which major constitution means 40A of the present embodiment, theimage display means 8, and the memory means 9 are integrated in thepresent embodiment, there may be employed such a configuration that theimage display means 8, or the image display means 8 and the memory means9 are separated from the major constitution means 40A as respectivelyindependent image display unit and storage unit.

The inter-voxel connection degree calculation means 44 calculates aconnection degree vector {right arrow over (T)}(l,m,k) for adjusting areference value (test value, significance level, or the like) uponperforming various data tests (for example, t-test, f-test, or z-test)for every voxel on the time series data ρ(l, m, k, i) of the brainfunction information which is pre-processed by the data preprocessingmeans 3 as a function of the averaged diffusion degree vector {rightarrow over (D)}′(l,m,k) of Equation (12) from the diffusion tensor dataD(l, m, k) (Equation (9)) acquired by the diffusion tensor dataacquisition means 2.

The data test means 400 corrects the reference value based on theconnection degree vector {right arrow over (T)}(l,m,k) calculated by theinter-voxel connection degree calculation means 44, and performs thedata test for every voxel on the brain function data ρ(l, m, k) afterthe preprocessing based on the corrected reference value.

FIG. 21 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus 40 shown in FIG. 20.

At Step S300, the brain function data acquisition means 1 acquires theoriginal time series data ρ′(l, m, k, i) of the brain functioninformation measured by the MRI device 50. At the same step, thediffusion tensor information acquisition means 2 acquires the diffusiontensor data D(l, m, k) measured also by the MRI device 50.

Next, at Step S310, the data preprocessing means 3 performs thepreprocessing (a) to (d) as explained in full detail in the firstembodiment on the original time series data ρ′(l, m, k, i) of the brainfunction information acquired at Step S300 to thereby generate the timeseries data ρ(l, m, k, i) of the brain function information.

Next, at Step S320, the inter-voxel connection degree calculation means44 calculates a connection degree vector:

{right arrow over (T)}(l,m,k)=(T _(l)(l,m,k),T _(m)(l,m,k),T_(k)(l,m,k))  (38)

for adjusting the reference value (test value, significance level, orthe like) upon performing, for example, the z-test, for every voxel, onthe time series data ρ(l, m, k, i) of the brain function informationpre-processed at Step S310 as a function of the averaged diffusiondegree vector {right arrow over (D)}′(l,m,k) of Equation (12), from thediffusion tensor data D(l, m, k) acquired at Step S310.

Next, at Step S330, the data test means 400 performs, for every voxel,the z-test on the time series data ρ(l, m, k, i) of the brain functioninformation pre-processed at Step S310 to thereby calculate a z score.Subsequently, at the same step, using a voxel with high calculated zscore (for example, voxel (l, m, k)) as a reference point, a valuea(l+1, m, k) of a significance level in a voxel (for example, voxel(l+1,m, k)) of a periphery (26 directions) of the reference point is loweredas, for example,

a′(l+1,m,k)={1−αT ₁(l,m,k)}a(l+1,m,k)  (39)

where α is a weighting coefficient, depending on to the connectiondegree vector {right arrow over (T)}(l,m,k) calculated at Step S320.

Next, at Step S340, the image generation means 7 generates a value basedon the brain function data ρ(l, m, k) on the voxel determined to besignificant as the image data based on the significance level correctedat Step S330.

Subsequently, at Step S350, the image display means 8 stereoscopicallydisplays the image data generated at Step S340.

Note that although the value of the significance level of the test isadjusted using the connection degree vector {right arrow over(T)}(l,m,k) of Equation (38) calculated from the diffusion tensor dataD(l, m, k) acquired by the diffusion tensor data acquisition means 2 inthe above, the test value may be adjusted instead of that.

For example, a case where the z-test is performed between a task imageand a rest image for every voxel is considered. The task image means animage when performing a certain task (for example, slice images of 1 to3 represented with the thick line in FIG. 2), while the rest image meansan image when not performing the task (for example, slice images of 5 to7 represented with the thick line in FIG. 2). In this case, the z score(test value of the z-test) in the voxel (l, m, k) is typicallyrepresented as

$\begin{matrix}{{z\left( {l,m,k} \right)} = \frac{{{M\; {t\left( {l,m,k} \right)}} - {M\; {c\left( {l,m,k} \right)}}}}{\sqrt{{\sigma \; {t\left( {l,m,k} \right)}^{2}} + {\sigma \; {c\left( {l,m,k} \right)}^{2}}}}} & (40)\end{matrix}$

where Mt(l, m, k) represents a mean value of the time series data ρ(l,m, k, i) of the brain function information of the task image; Mc(l, m,k), a mean value of the time series data ρ(l, m, k, i) of the brainfunction information of the rest image; σ_(t)(l, m, k), a standarddeviation of the time series data ρ(l, m, k, i) of the brain functioninformation of the task image; and σ_(c)(l, m, k), a standard deviationof the time series data ρ(l, m, k, i) of the brain function informationof the rest image. Under this premise, if z=0, it can be said that thereis no difference between the mean value of the time series data ρ(l, m,k, i) of the brain function information in the task image and the meanvalue of the time series data ρ(l, m, k, i) of the brain functioninformation in the rest image. If Z=1, it means that the differencebetween the mean value of time series data ρ(l, m, k, i) of the brainfunction information in the task image and the mean value of time seriesdata ρ(l, m, k, i) of the brain function information in the rest imageis larger by a variation of the time series data. From such a tendency,it is thinkable that a voxel with large value of z score is an activeregion associated with the task.

For that reason, Equation (40) is changed as shown in

$\begin{matrix}{{{z\left( {l,m,k} \right)} = {{b\left( {l,m,k} \right)}\frac{{{M\; {t\left( {l,m,k} \right)}} - {M\; {c\left( {l,m,k} \right)}}}}{\sqrt{{\sigma \; {t\left( {l,m,k} \right)}^{2}} + {\sigma \; {c\left( {l,m,k} \right)}^{2}}}}}},} & (41)\end{matrix}$

and b(l, m, k) is corrected at Step S330 to

b′(l,m,k)={1+βT _(l)(l,m,k)}b(l,m,k)  (42)

where β is a weighting coefficient, using the connection degree vector{right arrow over (T)}(l,m,k) of Equation (38) calculated at Step S320.

Next, at Step S340, the image generation means 7 generates a value basedon the brain function data ρ(l, m, k) on the voxel determined to besignificant as the image data based on the common significance level inall the voxels. Subsequently, at Step S350, the image display means 8stereoscopically displays the image data generated at Step S340.

As described above, in the fourth embodiment, the value of the referencevalue (test value or significance level) upon testing the time seriesdata of the brain function information which is acquired also from theMRI device has been adjusted for every voxel based on the diffusiontensor data acquired from the MRI device, so that activated brainregions can be located also in consideration of the connection structurebetween the activated brain regions.

Modified Example of Fourth Embodiment

In the fourth embodiment, when various data tests (for example, T-test,F-test, or Z-test) are performed on the time series data ρ(l, m, k, i)of the brain function information for every voxel, the reference value(test value, significance level, or the like) of the data test isadjusted using the connection degree vector {right arrow over(T)}(l,m,k) calculated from the diffusion tensor data D(l, m, k) whichis acquired by the diffusion tensor data acquisition means 2, but thereis also considered a situation in achieving the object of the presentinvention such that when the data analysis is performed on the timeseries data ρ(l, m, k, i) of the brain function information for everyvoxel by various kinds of analysis techniques, a data test (for example,T-test, an F-test, or Z-test) is performed on whether or not theanalysis technique is statistically significant.

Accordingly, as the present modified embodiment, it is considered toadjust the reference value (test value, significance level, or the like)in that case using the connection degree vector {right arrow over(T)}(l,m,k).

Incidentally, only portions different from those of the fourthembodiment will be hereinafter described in order to avoid duplication.

FIG. 22 is a conceptual diagram showing a schematic configuration of abrain function analysis apparatus in accordance with the modifiedexample of the fourth embodiment. A configuration of a brain functionanalysis apparatus 40′ in accordance with the present modifiedembodiment has a configuration in which data test means 400′ and dataanalysis means 46′ are added thereto instead of the data test means 400in the configuration of the fourth embodiment.

The data analysis means 46′ performs data analysis on the time seriesdata ρ(l, m, k, i) of the brain function information which is acquiredby the brain function data acquisition means 1 and is also pre-processedby the data preprocessing means 3 for every voxel using the analysistechniques, such as conventional classification, regression, andcorrelation.

The data test means 400′ adjusts the reference value (test value,significance level, or the like) upon testing whether or not theanalysis technique performed by the data analysis means 46′ isstatistically significant based on various data test (for example,T-test, F-test, or Z-test) using the connection degree vector {rightarrow over (T)}(l,m,k).

FIG. 23 is a flow chart showing one example of a data analysis procedureperformed by the brain function analysis apparatus shown in FIG. 22.

At Step S360, the data analysis means 46′ performs data analysis on thetime series data ρ(l, m, k, i) of the brain function information whichis acquired by the brain function data acquisition means 1 and is alsopre-processed by the data preprocessing means 3 for every voxel usingthe analysis techniques, such as conventional classification,regression, and correlation.

1. Classificational analysis techniques (discriminant analysis, decisiontree, support vector machine, or the like)

1-1: In a case of the discriminant analysis, data classification isperformed using various kinds of discrimination functions, while usingthe explaining variable as the time series data ρ(l, m, k, i) of thebrain function information, and the external criterion as the timeseries data of the task and the rest.

1-2: In a case of the decision tree, data classification is performedusing the decision tree, while using the explaining variable (attribute)as the time series data of the task and the rest and forming severalclasses by discretizing the time series data ρ(l, m, k, i) of the brainfunction information as the external criterion.

2. Regressive analysis technique (regression analysis using variouskinds of functions (linear, non-linear))

2-1: In a case of the linear regression analysis, data analysis isperformed using a linear regression equation while using the explainingvariable as the task and the rest, and the explained variable as thetime series data ρ(l, m, k, i) of the brain function information.

3. Correlative analysis technique (technique of taking correlationbetween an estimated model (function) and actual data)

Data analysis is performed by taking correlation between a model set inadvance (for example, function such that stimulation is an input and thetime series data of the brain function information is an output) and theactual time series data ρ(l, m, k, i) of the brain function information.

Subsequently, at Step S331 through Step S320 (similar to that of thefourth embodiment), the data test means 400′ tests whether or not theresult obtained at Step S360 is statistically significant. At this time,the reference value (test value, significance level, or the like) of thetest is adjusted using the connection degree vector {right arrow over(T)}(l,m,k) (Equation (38)).

For example, when the f-test is performed on the result obtained by theclassificational analysis technique, the f-test is performed for everyvoxel on the result obtained at Step S360 to thereby calculate an fvalue. Subsequently, at the same step, using a voxel with highcalculated f value (for example, voxel (l, m, k)) as a reference point,a value a(l+1, m, k) of a significance level in a voxel (for example,voxel (l+1, m, k)) of a periphery (26 directions) of the reference pointis adjusted using, for example Equation (39) depending on to theconnection degree vector {right arrow over (T)}(l,m,k) calculated atStep S320.

In the case of the regressive analysis technique, the value of thethreshold (corresponding to the significance level) may be adjusted asdescribed above, while, in the case of the correlative analysistechnique, the threshold of the correlation may be adjusted as describedabove.

Next, at Step S340, the image generation means 7 generates a value basedon the brain function data ρ(l, m, k) on the voxel determined to besignificant as the image data based on the significance level correctedat Step S330.

Subsequently, at Step S350, the image display means 8 stereoscopicallydisplays the image data generated at Step S340.

Thus, effects similar to those of the third embodiment are obtained alsoin the present modified embodiment.

The above described embodiments are only exemplification, and thepresent invention is not limited to these.

For example, in each embodiment, the step of calculating the connectiondegree between the adjacent voxels may be performed just after the stepof acquiring the brain function data and the diffusion tensor data.

Additionally, also as for the data analysis technique, it is possible toapply a wavelet analysis, a logistic regression analysis, or the likethereto.

Moreover, it is also possible to incorporate the second or the thirdembodiment into the first embodiment. First, when the second embodimentis incorporated into the first embodiment, it is only necessary toemploy a configuration in which the brain function analysis apparatus 10shown in FIG. 1 is further provided with data smoothing means which hasthe same function as that of the data smoothing means 200 or 200′ shownin FIG. 10 or FIG. 13. Subsequently, what is necessary is that, afterthe data analysis means 6 determines a regression coefficient tominimize the data evaluation value Q at Step S50 of the firstembodiment, the above data smoothing means is applied to the value ofthe regression coefficient to thereby smooth the value of the regressioncoefficient anisotropically. As a result of this, it is possible togenerate images in which a degree of activity of the white matter (nervefiber) is more emphasized as compared with the images obtained by thefirst embodiment.

Meanwhile, when the third embodiment is incorporated into the firstembodiment, it is only necessary to employ a configuration in which thebrain function analysis apparatus 10 shown in FIG. 1 is further providedwith clustering means which has the same function as that of theclustering means 300 or 300′ shown in FIG. 15 or FIG. 19. Subsequently,what is necessary is that, after the data analysis means 6 determines aregression coefficient to minimize the data evaluation value Q at StepS50 of the first embodiment, the above clustering means is applied tothe value of the regression coefficient to thereby cluster the voxels.As a result of this, it is possible to generate images in which a degreeof activity of the white matter (nerve fiber) is more emphasized ascompared with the images obtained by the first embodiment.

Furthermore, it is also possible to extend the activated regions of thewhite matter voxel with respect to the results obtained by each of aboveembodiments by further using the diffusion tensor data D(l, m, k). Thetechnique is basically similar to that of the tractography.

Further, it is also possible to catch the activation of the white matterby applying the above-described techniques (regression analysis,correlation, classification, independent component analysis, test, orthe like) only to the white matter voxels.

Meanwhile, although the block design has been assumed as the experimentin each embodiment, the present invention is applicable to anevent-related fMRI.

In addition, the present invention is applicable to a ConnectivityAnalysis. There are several techniques in the Connectivity Analysis, andin the case of, for example, SEM (Structural Equation Modeling) (orcovariance structure analysis), correlation and test are performed onthe way of computation, but the present invention is applicable in thatcase, and smoothing and clustering are also applicable thereto.

The information on a connection between neurons and nerve fibers hasbeen acquired from the diffusion tensor data in the above, but if thereis information (knowledge) on the connection which has been anatomicallyknown already, it is also possible to use it.

Various embodiments other than those described above are possiblewithout departing from the subject matter of the present invention, andthe present invention is specified only by claims.

Finally, effects of the brain function analysis using the brain functionanalysis method of the present invention will be shown. FIG. 24 is aview showing results of various kinds of brain function analyses whenthe subject is made to perform simple repetition using acousticstimulations. In the experiment, to repeat a voice coming fromheadphones as it is heard is defined as the task, and a situation wherethe subject thinks nothing as less as possible is defined as the rest. Avolume number (measurement frequency) of the task is set to 48, a volumenumber (measurement frequency) of the rest is set to 60, and a volumenumber (measurement frequency) which is not used for the analysis is setto 12 (refer to FIG. 2). Additionally, TR (Time of Repetition:corresponding to a volume width in FIG. 2) is set to 5000 milliseconds.While the number of subjects is eight, analysis results of the subjectsfrom whom excellent results are obtained in both of SPM and the brainfunction analysis method of the present invention will be shownhereinafter.

FIGS. 24A and 24B both show results of the T-test using SPM, whereinFIG. 24A shows analysis results when the correction is performed on thethreshold of the T-test, and FIG. 12B shows analysis results when thecorrection is not performed on the threshold of the T-test. FIG. 24Cshows analysis results according to the analysis technique combining SPMand the tractography, while FIG. 24D shows analysis results using thebrain function analysis method in accordance with the first embodimentof the present invention. Note that, position compensation,normalization, and smoothing are performed as the preprocessing of SPM.A half-value width of the smoothing is set to 9 millimeters.Additionally, dTV is used for the tractography as software for diffusiontensor analysis.

As a premise, there are anatomically known that a Wernicke area (sensoryspeech area) and a Broca's area (motor speech area) are activated duringthe simple repetition, and both areas are connected by a bundle of nervefibers called arcuate fasciculus. This arcuate fasciculus is used duringutterance.

Even when SPM is used, the activation of the Wernicke area and theBroca's area cannot be detected when the threshold of the T-test iscorrected as shown in FIG. 24A. Meanwhile, when SPM is used and thethreshold of the T-test is not corrected, the activation of both areasis detected, but the arcuate fasciculus which connects both areas cannotbe detected as shown in FIG. 24B. In contrast to these results, if theanalysis technique combining SPM and the tractography is used, not onlythe activation of the Wernicke area and the Broca's area but also thearcuate fasciculus itself is detected as shown in FIG. 24C.

Meanwhile, when the brain function analysis method in accordance withthe first embodiment of the present invention is used, it is possible todetect even activation of the arcuate fasciculus (pulse transmission inthe nerve fiber constituting the arcuate fasciculus) in addition to thedetection of the activation of both areas and the arcuate fasciculuswhich connects both areas as shown in the FIG. 24D.

Moreover, in the analysis technique combining SPM and the tractography,an analyst has to specify a trace start point and a trace end point ofthe nerve fiber in advance in detecting the nerve fiber (arcuatefasciculus in this case). Hence, this technique will not be effective insuch a case that the trace start point and the trace end point of thenerve fiber cannot be specified in advance (for example, in such a taskthat an active region of the gray matter is not determined in advance).In contrast to that, according to the brain function analysis method inaccordance with the first embodiment of the present invention, it ispossible to detect the activation of the nerve fiber as shown in theFIG. 24D even when the trace start point and the trace end point of thenerve fiber cannot be specified in advance.

INDUSTRIAL APPLICABILITY

As described above, according to the present invention, as well as beingable to specify the activated brain region, it is also possible tospecify the nerve fiber (connection structure between active regions)even when the analyst does not specify the start point and an the pointof the nerve fiber in advance, and it is further possible to detect theactivation of the nerve fiber (pulse transmission in the nerve fiber),thus allowing the extremely effective brain function analysis method andbrain function analysis program to be provided for medical diagnoses andtreatments of higher brain dysfunction such as dementia, aphasia,schizophrenia, or the like.

1. A brain function data analysis method, comprising: constituting anevaluation value of a connection degree between adjacent voxels based ondiffusion tensor data capable of specifying a degree of diffusion ofprotons within the brain acquired on a voxel-by-voxel basis; analyzingbrain function data capable of specifying activated brain regionsacquired on the voxel-by-voxel basis, based on the evaluation value ofthe connection degree between the adjacent voxels.
 2. The brain functiondata analysis method according to claim 1, wherein analyzing the brainfunction data comprises calculating either of a minimum value and amaximum value of an evaluation value in which the evaluation value ofthe connection degree between the adjacent voxels is incorporated intothe evaluation value for every voxel of the brain function data by aregression analysis in which the brain function data and either of tasksare used as an explaining variable, and the brain function data and theother task are used as an explained variable.
 3. The brain function dataanalysis method according to claim 1, wherein analyzing the brainfunction data comprises adjusting a reference value of the test of thebrain function data based on the evaluation value of the connectiondegree between the adjacent voxels.
 4. The brain function data analysismethod according to claim 1, wherein analyzing the brain function datacomprises adjusting a reference value of the classification of the brainfunction data based on the evaluation value of the connection degreebetween the adjacent voxels.
 5. The brain function data analysis methodaccording to claim 1, wherein analyzing the brain function datacomprises adjusting a reference value of the correlation of the brainfunction data with a predetermined model based on the evaluation valueof the connection degree between the adjacent voxels.
 6. The brainfunction data analysis method according to claim 1, wherein analyzingthe brain function data comprises adjusting a reference value of anextraction which extracts main components from the brain function databased on the evaluation value of the connection degree between theadjacent voxels.
 7. The brain function data analysis method according toclaim 1, wherein analyzing the brain function data comprises smoothingthe brain function data based on the evaluation value of the connectiondegree between the adjacent voxels.
 8. The brain function data analysismethod according to claim 1, wherein analyzing the brain function datacomprises clustering the brain function data based on the evaluationvalue of the connection degree between the adjacent voxels.
 9. The brainfunction data analysis method according to claim 1, further comprisingpre-processing to transmit a value of the brain function data of theactivated region voxel which is located from the brain function data, toa value of the brain function data of another voxel based on thediffusion tensor data.
 10. A brain function analysis program causing acomputer to function as: brain function data acquisition means foracquiring, on a voxel-by-voxel basis, brain function data capable oflocating activated brain regions; diffusion tensor data acquisitionmeans for acquiring, on a voxel-by-voxel basis, diffusion tensor datacapable of specifying a degree of diffusion of protons within the brain;data evaluation value constitution means for constituting an evaluationvalue of the connection degree between adjacent voxels based on thediffusion tensor data; and data analysis means for analyzing the brainfunction data based on the evaluation value of the connection degreebetween the adjacent voxels.
 11. The brain function analysis programaccording to claim 10, wherein the data analysis means is regressionanalysis means in which the brain function data and either of tasks areused as an explaining variable, and the brain function data and theother task are used as an explained variable, and either of a minimumvalue and a maximum value of an evaluation value in which the evaluationvalue of the connection degree between the adjacent voxels isincorporated into the evaluation value for every voxel of the brainfunction data is calculated.
 12. The brain function analysis programaccording to claim 10, wherein the data analysis means is means fortesting the brain function data, and a reference value of the test isadjusted based on the evaluation value of the connection degree betweenthe adjacent voxels.
 13. The brain function analysis program accordingto claim 10, wherein the data analysis means is means for classifyingthe brain function data, and a reference value of the classification isadjusted based on the evaluation value of the connection degree betweenthe adjacent voxels.
 14. The brain function analysis program accordingto claim 10, wherein the data analysis means is means for correlatingthe brain function data with a predetermined model, and a referencevalue of the correlation is adjusted based on the evaluation value ofthe connection degree between the adjacent voxels.
 15. The brainfunction analysis program according to claim 10, wherein the dataanalysis means is means for extracting a main component from the brainfunction data, and a reference value of the extraction is adjusted basedon the evaluation value of the connection degree between the adjacentvoxels.
 16. The brain function analysis program according to claim 10,wherein the data analysis smoothes the brain function data based on theevaluation value of the connection degree between the adjacent voxels.17. The brain function analysis program according to claim 10, whereinthe data analysis means clusters the brain function data based on theevaluation value of the connection degree between the adjacent voxels.18. The brain function analysis program according to claim 10, whereinthe program causes the computer to function as data preprocessing meansfor pre-processing to transmit a value of the brain function data of theactivated region voxel which is located from the brain function dataacquired by the brain function data acquisition means to a value of thebrain function data of another voxel based on the diffusion tensor dataacquired by the diffusion tensor data acquisition means.
 19. A brainfunction analysis apparatus comprising: brain function data acquisitionmeans for acquiring, on a voxel-by-voxel basis, brain function datacapable of locating activated brain regions; diffusion tensor dataacquisition means for acquiring, on a voxel-by-voxel basis, diffusiontensor data capable of specifying a degree of diffusion of protonswithin the brain; data evaluation value constitution means forconstituting an evaluation value of the connection degree betweenadjacent voxels based on the diffusion tensor data; and data analysismeans for analyzing the brain function data based on the evaluationvalue of the connection degree between the adjacent voxels.
 20. Thebrain function analysis apparatus according to claim 19, wherein thedata analysis means is regression analysis means in which the brainfunction data and either of tasks are used as an explaining variable,and the brain function data and the other task are used as an explainedvariable, and either of a minimum value and a maximum value of anevaluation value in which the evaluation value of the connection degreebetween the adjacent voxels is incorporated into the evaluation valuefor every voxel of the brain function data is calculated.
 21. The brainfunction analysis apparatus according to claim 19, wherein the dataanalysis means is means for testing the brain function data, and areference value of the test is adjusted based on the evaluation value ofthe connection degree between the adjacent voxels.
 22. The brainfunction analysis apparatus according to claim 19, wherein the dataanalysis means is means for classifying the brain function data, and areference value of the classification is adjusted based on theevaluation value of the connection degree between the adjacent voxels.23. The brain function analysis apparatus according to claim 19, whereinthe data analysis means is means for correlating the brain function datawith a predetermined model, and a reference value of the correlation isadjusted based on the evaluation value of the connection degree betweenthe adjacent voxels.
 24. The brain function analysis apparatus accordingto claim 19, wherein the data analysis means is means for extracting amain component from the brain function data, and a reference value ofthe extraction is adjusted based on the evaluation value of theconnection degree between the adjacent voxels.
 25. The brain functionanalysis apparatus according to claim 19, further comprising datasmoothing means for smoothing the brain function data based on theevaluation value of the connection degree between the adjacent voxels.26. The brain function analysis apparatus according to claim 19, furthercomprising data clustering means for clustering the brain function databased on the evaluation value of the connection degree between theadjacent voxels.
 27. The brain function analysis apparatus according toclaim 19, further comprising data preprocessing means for pre-processingto transmit a value of the brain function data of the activated regionvoxel which is located from the brain function data acquired by thebrain function data acquisition means to a value of the brain functiondata of another voxel based on the diffusion tensor data acquired by thediffusion tensor data acquisition means.